An Overview Of Character Recognition
---
paper_title: Optical Character Recognition

paper_content:

From the Publisher: ::: Optical Character Recognition (OCR) has become an important and widely used technology. Among its many practical applications are the scanners used at store check-out counters, money changing machines, office scanning machines, and the efforts to automate the postal system. Research is particularly active in Japan where one important goal is to develop economical machines that can read Kanji characters. Such machines will be widely used in offices as man-machine interfaces. Dr. Mori from the Ricoh R&D Center in Japan has used his experience in optical character recognition to create a thorough reference to this widely used but still growing technology.
---
paper_title: A Syntactic Approach for Handwritten Mathematical Formula Recognition

paper_content:

Mathematical formulas are good examples of two-dimensional patterns as well as pictures or graphics. The use of syntactic methods is useful for interpreting such complex patterns. In this paper we propose a system for the interpretation of 2-D mathematic formulas based on a syntactic parser. This system is able to recognize a large class of 2-D mathematic formulas written on a graphic tablet. It starts the parsing by localization of the ``principal'' operator in the formula and attempts to partition it into subexpressions which are similarly analyzed by looking for a starting character. The generalized parser used in the system has been developed in our group for continuous speech recognition and picture interpretation.
---
paper_title: An omnifont open-vocabulary OCR system for English and Arabic

paper_content:

We present an omnifont, unlimited-vocabulary OCR system for English and Arabic. The system is based on hidden Markov models (HMM), an approach that has proven to be very successful in the area of automatic speech recognition. We focus on two aspects of the OCR system. First, we address the issue of how to perform OCR on omnifont and multi-style data, such as plain and italic, without the need to have a separate model for each style. The amount of training data from each style, which is used to train a single model, becomes an important issue in the face of the conditional independence assumption inherent in the use of HMMs. We demonstrate mathematically and empirically how to allocate training data among the different styles to alleviate this problem. Second, we show how to use a word-based HMM system to perform character recognition with unlimited vocabulary. The method includes the use of a trigram language model on character sequences. Using all these techniques, we have achieved character error rates of 1.1 percent on data from the University of Washington English Document Image Database and 3.3 percent on data from the DARPA Arabic OCR Corpus.
---
paper_title: A high-accuracy syntactic recognition algorithm for handwritten numerals

paper_content:

A new set of topological features (primitives) for use with a syntactic classifier for high-accuracy recognition of handwritten numerals is proposed. The tree grammar used in this study makes it possible to achieve high-recognition speeds with minimal preprocessing of the test pattern.
---
paper_title: Computer recognition of arabic cursive scripts

paper_content:

Abstract The main objective of this paper is to design an Arabic text recognition system. This system basically includes a segmentation stage in order to recognize typewritten Arabic cursive words. The segmentation process is completely based on tracing the outer contour of a given word and calculating the distance between the extreme points of intersection of the contour with a vertical line. At the output of the segmentation stage, the cursive word is presented as a sequence of isolated character contours. The recognition problem thus reduces to that of classifying each character. A set of Fourier descriptors are obtained from the coordinate sequences of the outer contour of each character. A topological classifier is also used to classify the stress mark over or under the character contour. A reject option is introduced by the classifier so that incorrectly segmented characters are detected. The developed system has shown a recognition rate of 99%. This result guarantees the efficiency of the used Fourier descriptors in discriminating between Arabic characters.
---
paper_title: Character recognition—a review

paper_content:

The machine replication of human reading has been the subject of intensive research for more than three decades. A large number of research papers and reports have already been published on this topic. Many commercial establishments have manufactured recognizers of varying capabilities. Handheld, desk-top, medium-size and large systems costing as high as half a million dollars are available, and are in use for various applications. However, the ultimate goal of developing a reading machine having the same reading capabilities of humans still remains unachieved. So, there still is a great gap between human reading and machine reading capabilities, and a great amount of further effort is required to narrow-down this gap, if not bridge it. This review is organized into six major sections covering a general overview (an introduction), applications of character recognition techniques, methodologies in character recognition, research work in character recognition, some practical OCRs and the conclusions.
---
paper_title: Historical review of OCR research and development

paper_content:

Research and development of OCR systems are considered from a historical point of view. The historical development of commercial systems is included. Both template matching and structure analysis approaches to R&D are considered. It is noted that the two approaches are coming closer and tending to merge. Commercial products are divided into three generations, for each of which some representative OCR systems are chosen and described in some detail. Some comments are made on recent techniques applied to OCR, such as expert systems and neural networks, and some open problems are indicated. The authors' views and hopes regarding future trends are presented. >
---
paper_title: High accuracy optical character recognition using neural networks with centroid dithering

paper_content:

Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set. >
---
paper_title: Survey: omnifont-printed character recognition

paper_content:

This paper presents an overview of methods for recognition of omnifont printed Roman alphabet characters with various fonts, sizes and formats (plain, bold, etc.) from OCR system perspectives. First, it summarizes the current needs for optical printed character recognition (OPCR) in general, and then describes its importance for conversion between paper and electronic media. Current status of commercially available software and products for OPCR are briefly reviewed. Analysis indicates that the challenge we face in OPCR is far from being solved, and there is still a great gap between human needs and machine reading capabilities. Second, OPCR systems and algorithms are briefly reviewed and compared from the context of digital document processing for the following four stages: preprocessing of images, segmentation, recognition, and post-processing. Finally, possible research directions to improve the performance of OPCR systems are suggested, such as using an approach based on the combination of template matching and varieties of feature-based algorithms to recognize isolated characters, the use of multilayered architectures for OPCR, and parallel processing- based high-performance architectures.
---
paper_title: An overview of character recognition methodologies

paper_content:

Abstract This work presents an overview of character recognition methodologies that have evolved in this century. At first the scanning devices that are used in character recognition will be explained, then some points will be stressed on the major research works that have made a great impact in character recognition. From a methodological point of view we will present the different steps that have been employed in OCR. And finally the most important industrial character recognisers will be covered along with the character data bases that are used in testing the various algorithms.
---
paper_title: Pen computing: a technology overview and a vision

paper_content:

This work gives an overview of a new technology that is attracting growing interest in public as well as in the computer industry itself. The visible difference from other technologies is in the use of a pen or pencil as the primary means of interaction between a user and a machine, picking up the familiar pen and paper interface metaphor. From this follows a set of consequences that will be analyzed and put into context with other emerging technologies and visions.Starting with a short historical background and the technical advances that begin making Pen Computing a reality, the new paradigms created by Pen Computing will be explained and discussed. Handwriting recognition, mobility and global information access are other central topics. This is followed by a categorization and an overview of current and future systems using pens as their primary user interface component.
---
paper_title: Writer independent on-line handwriting recognition using an HMM approach

paper_content:

In this paper we describe a Hidden Markov Model (HMM) based writer independent handwriting recognition system. A combination of signal normalization preprocessing and the use of invariant features makes the system robust with respect to variability among di!erent writers as well as di!erent writing environments and ink collection mechanisms. A combination of point oriented and stroke oriented features yields improved accuracy. Language modeling constrains the hypothesis space to manageable levels in most cases. In addition a two-pass N-best approach is taken for large vocabularies. We report experimental results for both character and word recognition on several UNIPEN datasets, which are standard datasets of English text collected from around the world. ( 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
---
paper_title: Off-line cursive script word recognition

paper_content:

Cursive script word recognition is the problem of transforming a word from the iconic form of cursive writing to its symbolic form. Several component processes of a recognition system for isolated offline cursive script words are described. A word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words. A unique feature representation is generated bottom-up from the image using statistical dependences between letters and features. Ratings for partially formed words are computed using a stack algorithm and a lexicon represented as a trie. Several novel techniques for low- and intermediate-level processing for cursive script are described, including heuristics for reference line finding, letter segmentation based on detecting local minima along the lower contour and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features, and finding shape-oriented events. Experiments demonstrating the performance of the system are also described. >
---
paper_title: Building bilingual microcomputer systems

paper_content:

In the Arab world the need for bilingual microcomputer systems is ever increasing. In addition to the ability to process the Arabic and English scripts, an ideal system should support the use of existing applications with Arabic data and the access to the system facilities through Arabic interfaces. The Integrated Arabic System (IAS) was developed to study the feasibility of building such systems using existing microcomputers and software solutions.
---
paper_title: Research on Machine Recognition of Handprinted Characters

paper_content:

Machine recognition of handprinted Chinese characters has recently become very active in Japan. Both from the practical and the academic point of view, very encouraging results are reported. The work is described systematically and analyzed in terms of so-called feature matching, which is likely to be the mainstream of the research and development of machine recognition of handprinted Chinese characters. A database, ETL8 (881 Kanji, 71 hirakana, and 160 variations for each category), is explained, on which many experiments were performed. Recognition rates reported using this database can be compared, and so somewhat qualitative evaluation of these methods is described. Based on the comparative study, the merits and demerits of both feature and structural matching are discussed and some future directions are mentioned.
---
paper_title: Personal identification based on handwriting

paper_content:

Abstract Many techniques have been reported for handwriting-based writer identification. The majority of techniques assume that the written text is fixed (e.g., in signature verification). In this paper we attempt to eliminate this assumption by presenting a novel algorithm for automatic text-independent writer identification. Given that the handwriting of different people is often visually distinctive, we take a global approach based on texture analysis, where each writer's handwriting is regarded as a different texture. In principle, this allows us to apply any standard texture recognition algorithm for the task (e.g., the multi-channel Gabor filtering technique). Results of 96.0% accuracy on the classification of 1000 test documents from 40 writers are very promising. The method is shown to be robust to noise and contents.
---
paper_title: A Hidden Markov Model approach to online handwritten signature verification

paper_content:

A method for the automatic verification of online handwritten signatures using both global and local features is described. The global and local features capture various aspects of signature shape and dynamics of signature production. We demonstrate that adding a local feature based on the signature likelihood obtained from Hidden Markov Models (HMM), to the global features of a signature, significantly improves the performance of verification. The current version of the program has 2.5% equal error rate. At the 1% false rejection (FR) point, the addition of the local information to the algorithm with only global features reduced the false acceptance (FA) rate from 13% to 5%.
---
paper_title: Integration of hand-written address interpretation technology into the United States Postal Service Remote Computer Reader system

paper_content:

Hand-written address interpretation (HWAI) technology has been recently incorporated into the processing of letter mail by the US Postal Service. The Remote Bar Coding System, which is an image management system for assigning bar codes to mail that has not been fully processed by postal OCR equipment, has been retrofitted with a Remote Computer Reader (RCR), into which the HWAI technology is integrated. A description of the HWAI technology, including its algorithms for the control structure, recognizers and databases, is provided. Its performance on more than a million hand-written mail-pieces in a field deployment of the integrated RCR-HWAI system is indicated. Future enhancements for a nationwide deployment of the system are indicated.
---
paper_title: The A2iA Intercheque System: Courtesy Amount and Legal Amount Recognition for French Checks

paper_content:

We developed a check reading system, termed INTERCHEQUE, which recognizes both the legal (LAR) and the courtesy amount (CAR) on bank checks. The version presented here is designed for the recognition of French, omni-bank, omni-scriptor, handwritten bank checks, and meets industrial requirements, such as high processing speed, robustness, and extremely low error rates. We give an overview of our recognition system and discuss some of the pattern recognition techniques used. We also describe an installation which processes of the order of 70,000 checks per day. Results on a data base of about 170,000 checks show a recognition rate of about 75% for an error rate of the order of 1/10,000 checks.
---
paper_title: NPen/sup ++/: a writer independent, large vocabulary on-line cursive handwriting recognition system

paper_content:

In this paper we describe the NPen/sup ++/ system for writer independent on-line handwriting recognition. This recognizer needs no training for a particular writer and can recognize any common writing style (cursive, hand-printed, or a mixture of both). The neural network architecture, which was originally proposed for continuous speech recognition tasks, and the preprocessing techniques of NPen/sup ++/ are designed to make heavy use of the dynamic writing information, i.e. the temporal sequence of data points recorded on an LCD tablet or digitizer. We present results for the writer independent recognition of isolated words. Tested on different dictionary sizes from 1,000 up to 100,000 words, recognition rates range from 98.0% for the 1,000 word dictionary to 91.4% on a 20,000 word dictionary and 82.9% for the 100,000 word dictionary. No language models are used to achieve these results.
---
paper_title: Automated forms-processing software and services

paper_content:

While document-image systems for the management of collections of documents, such as forms, offer significant productivity improvements, the entry of information from documents remains a labor-intensive and costly task for most organizations. In this paper, we describe a software system for the machine reading of forms data from their scanned images. We describe its major components: form recognition and “dropout,” intelligent character recognition (ICR), and contextual checking. Finally, we describe applications for which our automated forms reader has been successfully used.
---
paper_title: Content based internet access to paper documents

paper_content:

When archives of paper documents are to be accessed via the Internet, the implicit hypertext structure of the original documents should be employed. In this paper we study the different hypertext structures one encounters in a document. Methods for analyzing paper documents to find these structures are presented. The structures also form the basis for the presentation of the content of the document to the user. Results are presented.
---
paper_title: An omnifont open-vocabulary OCR system for English and Arabic

paper_content:

We present an omnifont, unlimited-vocabulary OCR system for English and Arabic. The system is based on hidden Markov models (HMM), an approach that has proven to be very successful in the area of automatic speech recognition. We focus on two aspects of the OCR system. First, we address the issue of how to perform OCR on omnifont and multi-style data, such as plain and italic, without the need to have a separate model for each style. The amount of training data from each style, which is used to train a single model, becomes an important issue in the face of the conditional independence assumption inherent in the use of HMMs. We demonstrate mathematically and empirically how to allocate training data among the different styles to alleviate this problem. Second, we show how to use a word-based HMM system to perform character recognition with unlimited vocabulary. The method includes the use of a trigram language model on character sequences. Using all these techniques, we have achieved character error rates of 1.1 percent on data from the University of Washington English Document Image Database and 3.3 percent on data from the DARPA Arabic OCR Corpus.
---
paper_title: Character recognition—a review

paper_content:

The machine replication of human reading has been the subject of intensive research for more than three decades. A large number of research papers and reports have already been published on this topic. Many commercial establishments have manufactured recognizers of varying capabilities. Handheld, desk-top, medium-size and large systems costing as high as half a million dollars are available, and are in use for various applications. However, the ultimate goal of developing a reading machine having the same reading capabilities of humans still remains unachieved. So, there still is a great gap between human reading and machine reading capabilities, and a great amount of further effort is required to narrow-down this gap, if not bridge it. This review is organized into six major sections covering a general overview (an introduction), applications of character recognition techniques, methodologies in character recognition, research work in character recognition, some practical OCRs and the conclusions.
---
paper_title: Off-line Arabic character recognition: the state of the art

paper_content:

Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic characters because of the complexity of printed and handwritten text, and this problem is still an open research field. The main objective of this paper is to present the state of Arabic character recognition research throughout the last two decades.
---
paper_title: On-Line and Off-Line Handwriting Recognition : A Comprehensive Survey

paper_content:

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.
---
paper_title: High accuracy optical character recognition using neural networks with centroid dithering

paper_content:

Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set. >
---
paper_title: Survey: omnifont-printed character recognition

paper_content:

This paper presents an overview of methods for recognition of omnifont printed Roman alphabet characters with various fonts, sizes and formats (plain, bold, etc.) from OCR system perspectives. First, it summarizes the current needs for optical printed character recognition (OPCR) in general, and then describes its importance for conversion between paper and electronic media. Current status of commercially available software and products for OPCR are briefly reviewed. Analysis indicates that the challenge we face in OPCR is far from being solved, and there is still a great gap between human needs and machine reading capabilities. Second, OPCR systems and algorithms are briefly reviewed and compared from the context of digital document processing for the following four stages: preprocessing of images, segmentation, recognition, and post-processing. Finally, possible research directions to improve the performance of OPCR systems are suggested, such as using an approach based on the combination of template matching and varieties of feature-based algorithms to recognize isolated characters, the use of multilayered architectures for OPCR, and parallel processing- based high-performance architectures.
---
paper_title: An overview of character recognition methodologies

paper_content:

Abstract This work presents an overview of character recognition methodologies that have evolved in this century. At first the scanning devices that are used in character recognition will be explained, then some points will be stressed on the major research works that have made a great impact in character recognition. From a methodological point of view we will present the different steps that have been employed in OCR. And finally the most important industrial character recognisers will be covered along with the character data bases that are used in testing the various algorithms.
---
paper_title: Optical Character Recognition

paper_content:

From the Publisher: ::: Optical Character Recognition (OCR) has become an important and widely used technology. Among its many practical applications are the scanners used at store check-out counters, money changing machines, office scanning machines, and the efforts to automate the postal system. Research is particularly active in Japan where one important goal is to develop economical machines that can read Kanji characters. Such machines will be widely used in offices as man-machine interfaces. Dr. Mori from the Ricoh R&D Center in Japan has used his experience in optical character recognition to create a thorough reference to this widely used but still growing technology.
---
paper_title: Research on Machine Recognition of Handprinted Characters

paper_content:

Machine recognition of handprinted Chinese characters has recently become very active in Japan. Both from the practical and the academic point of view, very encouraging results are reported. The work is described systematically and analyzed in terms of so-called feature matching, which is likely to be the mainstream of the research and development of machine recognition of handprinted Chinese characters. A database, ETL8 (881 Kanji, 71 hirakana, and 160 variations for each category), is explained, on which many experiments were performed. Recognition rates reported using this database can be compared, and so somewhat qualitative evaluation of these methods is described. Based on the comparative study, the merits and demerits of both feature and structural matching are discussed and some future directions are mentioned.
---
paper_title: Boundary detection using mathematical morphology

paper_content:

Object boundaries contain important shape information in an image. Mathematical morphology is shape sensitive and can be used in boundary detection. In this paper, we propose dynamic mathematical morphology which only operates on the parts of interest in an image and reacts to certain characteristics of the region. The next position of the structuring element is dynamically selected at each step of the operation. The technique is used to detect object boundaries and has produced encouraging results.
---
paper_title: Recognizing off-line cursive handwriting

paper_content:

We present a system for recognizing off-line, cursive, English text, guided in part by global characteristics (style) of the handwriting. We introduce a new method for segmenting words into letters, based on minimizing a cost function. Segmented letters are normalized with a novel algorithm that scales different parts of a letter separately removing much of the variation in the writing. We use a neural network for letter recognition and use the output of the network as posterior probabilities of letters in the word recognition process. We found that using a hidden Markov Model for word recognition is less successful than assuming an independent process for our small set of test words. In our experiments with several hundred words, written by 7 writers, 96% of the test words were correctly segmented, 52% were correctly recognized, and 70% were in the top three choices. >
---
paper_title: A method of detecting the orientation of aligned components

paper_content:

This report describes a method of detecting the orientation of a set components which are located along parallel lines. the orientation is detected by using the histogram of nearest neighbor directions. Examples are given using three kinds of images: envelope images, Glass patterns of dots, and aerial photographs of houses.
---
paper_title: Document image defect models

paper_content:

A lack of explicit quantitative models of imaging defects due to printing, optics, and digitization has retarded progress in some areas of document image analysis, including syntactic and structural approaches. Establishing the essential properties of such models, such as completeness (expressive power) and calibration (closeness of fit to actual image populations) remain open research problems. Work-in-progress towards a parameterized model of local imaging defects is described, together with a variety of motivating theoretical arguments and empirical evidence. A pseudo-random image generator implementing the model has been built. Applications of the generator are described, including a polyfont classifier for ASCII and a single-font classifier for a large alphabet (Tibetan U-Chen), both of which which were constructed with a minimum of manual effort. Image defect models and their associated generators permit a new kind of image database which is explicitly parameterized and indefinitely extensible, alleviating some drawbacks of existing databases.
---
paper_title: A robust skew detection algorithm for grayscale document image

paper_content:

A fast and robust skew detection algorithm for gray-scale images is presented. The MCCSD (modified cross-correlation skew detection) algorithm uses horizontal and vertical cross-correlation simultaneously to deal with vertically laid-out text, which is commonly used in Chinese or Japanese documents. Instead of calculating the correlation for the entire image, we use small randomly selected regions to speed up the process. The region verification stage and further processing of auxiliary peaks make our method robust and reliable. An experiment shows that the proposed method has good results in detecting skew in various kinds of pages.
---
paper_title: Adaptive, quadratic preprocessing of document images for binarization

paper_content:

This paper presents an adaptive algorithm for preprocessing document images prior to binarization in character recognition problems. Our method is similar in its approach to the blind adaptive equalization of binary communication channels. The adaptive filter utilizes a quadratic system model to provide edge enhancement for input images that have been corrupted by noise and other types of distortions during the scanning process. Experimental results demonstrating significant improvement in the quality of the binarized images over both direct binarization and a previously available preprocessing technique are also included.
---
paper_title: Repulsive attractive network for baseline extraction on document images

paper_content:

This paper describes a new framework, called repulsive attractive (RA) network for baseline extraction on document images. The RA network is a self organizing feature detector which interacts with the document text image through the attractive and repulsive forces defined among the network components and the document image. Experimental results indicate that the network can successfully extract the baselines under heavy noise and with overlaps between the ascending and descending portions of the characters of adjacent lines. The proposed method is also applicable to a wide range of image processing applications, such as curve fitting, segmentation and thinning.
---
paper_title: Recognition of cursive writing on personal checks

paper_content:

The system described in this paper applies Hidden Markov technology both to the task of recognizing the cursive legal amount on personal checks and the isolated (numeric) courtesy amount.
---
paper_title: Which Hough transform

paper_content:

Abstract The Hough transform is recognized as being a powerful tool in shape analysis which gives good results even in the presence of noise and occlusion. Major shortcomings of the technique are excessive storage requirements and computational complexity. Solutions to these problems form the bulk of contributions to the literature concerning the Hough transform. An excellent comprehensive review of available methods up to and partially including 1988 is given by Illingworth and Kittler (Comput. Vision Graphics Image Process. 44, 1988, 87-116). In the years following this survey much new literature has been published. The present work offers an update on state of the art Hough techniques. This includes comparative studies of existing techniques, new perspectives on the theory, very many novel algorithms, parallel implementations, and additions to the task-specific hardware. Care is taken to distinguish between research that aims to further basic understanding of the technique without necessarily being computationally realistic and research that may be applicable in an industrial context. A new trend in Hough transform work, that of the probabilistic Houghs, is identified and reviewed in some detail. Attempts to link the low level perceptive processing offered by the Hough transform to high level knowledge driven processing are also included, together with the many recent successful applications appearing in the literature.
---
paper_title: Morphological filtering: An overview

paper_content:

This paper is an overview on the concept of morphological filtering. Starting from openings and the associated granulometries, we discuss the notion and construction of morphological filters. Then the major differences between the ‘morphological’ and the ‘linear’ approaches are highlighted. Finally, the problem of optimal morphological filtering is presented.
---
paper_title: A new scheme for off-line handwritten connected digit recognition

paper_content:

A scheme is proposed for off-line handwritten connected digit recognition, which uses a sequence of segmentation and recognition algorithms. First, the connected digits are segmented by employing both the gray scale and binary information. Then, a new set of features is extracted from the segments. The parameters of the feature set are adjusted during the training stage of the hidden Markov model (HMM) where the potential digits are recognized. Finally, in order to confirm the preliminary segmentation and recognition results, a recognition based segmentation method is presented.
---
paper_title: Chaincode contour processing for handwritten word recognition

paper_content:

Contour representations of binary images of handwritten words afford considerable reduction in storage requirements while providing lossless representation. On the other hand, the one-dimensional nature of contours presents interesting challenges for processing images for handwritten word recognition. Our experiments indicate that significant gains are to be realized in both speed and recognition accuracy by using a contour representation in handwriting applications.
---
paper_title: Quality assessment and restoration of typewritten document images

paper_content:

We present a useful method for assessing the quality of a typewritten document image and automatically selecting an optimal restoration method based on that assessment. We use five quality measures that assess the severity of background speckle, touching characters, and broken characters. A linear classifier uses these measures to select a restoration method. On a 139-document corpus, our methodology reduced the corpus OCR character error rate from 20.27% to 12.60%.
---
paper_title: One dimensional representation of two dimensional information for HMM based handwritten recognition

paper_content:

We introduce a new set of one-dimensional discrete, constant length features to represent two dimensional shape information for HMM (hidden Markov model), based handwritten optical character recognition problem. The proposed feature set embeds the two dimensional information into a sequence of one-dimensional codes, selected from a code book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for HMM based shape recognition schemes. The new feature set is used in a handwritten optical character recognition scheme, where a sequence of segmentation and recognition stages is employed. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of the HMM. The proposed character recognition system is tested on both a locally generated cursively handwritten data and isolated number digits of NIST database. The experimental results indicate high recognition rates.
---
paper_title: Adaptive unsharp masking for contrast enhancement

paper_content:

A new scheme of unsharp masking for image contrast enhancement is presented. An adaptive algorithm is introduced so that a sharpening action is performed only in locations where the image exhibits significant dynamics. Hence, the amplification of noise in smooth areas is reduced. An adaptive directional filtering is also performed so as to provide suitable emphasis to the different directional characteristics of the detail. Because it is capable of treating high-detail and medium-detail areas differently, this algorithm also avoids unpleasant overshoot artifacts in regions of sharp transitions. Experimental results demonstrating the usefulness of the adaptive operator in an application involving preprocessing of images for enhancement prior to zooming are also included.
---
paper_title: Document image preprocessing based on optimal Boolean filters

paper_content:

Abstract In this paper, optimal Boolean filters are applied to enhance the binary document images corrupted with uniform noise or uniformly distributed distinct graphical patterns in the background. The performance and operation theory of optimal Boolean filters against other competitive techniques are compared. Experimental results show that the Boolean filters outperforms the morphology approach in extracting the text from overlapped text/background images. The feasibility of trained Boolean filters is also confirmed by experimental results in the case where the original image is not available.
---
paper_title: NORMALIZING AND RESTORING ON-LINE HANDWRITING

paper_content:

Abstract Preprocessing and normalization techniques for on-line handwriting analysis are crucial steps that usually compromise the success of recognition algorithms. These steps are often neglected and presented as solved problems, but this is far from the truth. An overview is presented of the principal on-line techniques for handwriting preprocessing and word normalization, covering the major difficulties encountered and the various approaches usually used to resolve these problems. Some measurable definitions for handwriting characteristics are proposed, such as baseline orientation, character slant and handwriting zones. These definitions are used to measure and quantify the performance of the normalization algorithms. An approach to enhancing and restoring handwriting text is also presented, and an objective evaluation of all the processing results.
---
paper_title: How to extend and bootstrap an existing data set with real-life degraded images

paper_content:

This paper introduces a methodology for bootstrapping and creating large number of groundtruthed "real-life" degraded images from an existing data set with a fraction of the original cost and time. The real-life degradations include geometric distortions, coffee stains, water or ink marks, and folds and creases. The methodology includes an automatic procedure to generate unlimited "real-life" degraded images (with coffee and ink marks and soil spots) without any cost. A small experiment was conducted to illustrate the effectiveness of our methodology. In the experiment, 22 real-life degraded images and the two original images were tested on a commercial OCR system. The accuracy rates of the OCR for the two original pages are 98.46% and 99.34% while the accuracy rates for the degraded pages are ranging from 57.17% to 98.45%, depending on the severity and the type of degradation applied to the pages.
---
paper_title: Off-line handwritten word recognition using a hidden Markov model type stochastic network

paper_content:

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported. >
---
paper_title: Off-line cursive script word recognition

paper_content:

Cursive script word recognition is the problem of transforming a word from the iconic form of cursive writing to its symbolic form. Several component processes of a recognition system for isolated offline cursive script words are described. A word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words. A unique feature representation is generated bottom-up from the image using statistical dependences between letters and features. Ratings for partially formed words are computed using a stack algorithm and a lexicon represented as a trie. Several novel techniques for low- and intermediate-level processing for cursive script are described, including heuristics for reference line finding, letter segmentation based on detecting local minima along the lower contour and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features, and finding shape-oriented events. Experiments demonstrating the performance of the system are also described. >
---
paper_title: Optimal Local Weighted Averaging Methods in Contour Smoothing

paper_content:

In several applications where binary contours are used to represent and classify patterns, smoothing must be performed to attenuate noise and quantization error. This is often implemented with local weighted averaging of contour point coordinates, because of the simplicity, low-cost and effectiveness of such methods. Invoking the "optimality" of the Gaussian filter, many authors will use Gaussian-derived weights. But generally these filters are not optimal, and there has been little theoretical investigation of local weighted averaging methods per se. This paper focuses on the direct derivation of optimal local weighted averaging methods tailored towards specific computational goals such as the accurate estimation of contour point positions, tangent slopes, or deviation angles. A new and simple digitization noise model is proposed to derive the best set of weights for different window sizes, for each computational task. Estimates of the fraction of the noise actually removed by these optimum weights are also obtained. Finally, the applicability of these findings for arbitrary curvature is verified, by numerically investigating equivalent problems for digital circles of various radii.
---
paper_title: A one-pass two-operation process to detect the skeletal pixels on the 4-distance transform

paper_content:

A skeletonizing procedure is illustrated that is based on the notion of multiple pixels as well as on the use of the 4-distance transform. The set of the skeletal pixels is identified within one sequential raster scan of the picture where the 4-distance transform is stored. Two local conditions, introduced to characterize the multiple pixels are employed. Since the set of the skeletal pixels is at most two pixels wide, the skeleton can be obtained on completion of an additional inspection of the picture, during which time standard removal operations are applied. Besides being correct and computationally convenient, the procedure produces a labeled skeleton, i.e. a skeleton whose adequacy for shape description purposes is generally acknowledged. >
---
paper_title: An adaptive logical method for binarization of degraded document images

paper_content:

Abstract This paper describes a modified logical thresholding method for binarization of seriously degraded and very poor quality gray-scale document images. This method can deal with complex signal-dependent noise, variable background intensity caused by nonuniform illumination, shadow, smear or smudge and very low contrast. The output image has no obvious loss of useful information. Firstly, we analyse the clustering and connection characteristics of the character stroke from the run-length histogram for selected image regions and various inhomogeneous gray-scale backgrounds. Then, we propose a modified logical thresholding method to extract the binary image adaptively from the degraded gray-scale document image with complex and inhomogeneous background. It can adjust the size of the local area and logical thresholding level adaptively according to the local run-length histogram and the local gray-scale inhomogeneity. Our method can threshold various poor quality gray-scale document images automatically without need of any prior knowledge of the document image and manual fine-tuning of parameters. It keeps useful information more accurately without overconnected and broken strokes of the characters, and thus, has a wider range of applications compared with other methods.
---
paper_title: Integral ratio: a new class of global thresholding techniques for handwriting images

paper_content:

We propose a class of histogram based global thresholding techniques called integral ratio. They are designed to threshold gray-scale handwriting images and separate the handwriting from the background. The following tight requirements must be met: 1) all the details of the handwriting are to be retained, 2) the writing paper used may contain strong colored and/or patterned background which must be removed, and 3) the handwriting may be written using a wide variety of pens such as a fountain pen, ballpoint pen, or pencil. A specific application area which requires these tight requirements is forensic document examination, where a handwritten document is often considered as legal evidence and the handwriting must not be tampered with or modified in any way. The proposed class of techniques is based on a two stage thresholding approach requiring each pixel of a handwritten image to be placed into one of three classes: foreground, background, and a fuzzy area between them where it is hard to determine whether a pixel belongs to the foreground or the background. Two techniques, native integral ratio (NIR) and quadratic integral ratio (QIR), were created based on this class and tested against two well-known thresholding techniques: Otsu's (1979) technique and the entropy thresholding technique. We found that QIR has superior performance compared to all the other techniques tested.
---
paper_title: An introduction to digital image processing

paper_content:

A new and distinct spur type apple variety which originated as a limb mutation of the standard winter banana apple tree (non-patented) is provided. This new apple variety possesses a vigorous compact and only slightly spreading growth habit and can be distinguished from its parent and the Housden spur type winter banana apple variety (non-patented). More specifically, the new variety forms more fruiting spurs per unit length on two and three year old wood than the standard winter banana apple tree and less spurs per unit length than the Housden spur type winter banana apple tree. Additionally, the new variety has the ability to heavily bear fruit having a whitish-yellow skin color with a sometimes slight scarlet red blush upon maturity which is substantially identical to that of the standard winter banana apple tree and which has substantially less skin russeting than the Housden spur type winter banana apple tree.
---
paper_title: Goal-Directed Evaluation of Binarization Methods

paper_content:

This paper presents a methodology for evaluation of low-level image analysis methods, using binarization (two-level thresholding) as an example. Binarization of scanned gray scale images is the first step in most document image analysis systems. Selection of an appropriate binarization method for an input image domain is a difficult problem. Typically, a human expert evaluates the binarized images according to his/her visual criteria. However, to conduct an objective evaluation, one needs to investigate how well the subsequent image analysis steps will perform on the binarized image. We call this approach goal-directed evaluation, and it can be used to evaluate other low-level image processing methods as well. Our evaluation of binarization methods is in the context of digit recognition, so we define the performance of the character recognition module as the objective measure. Eleven different locally adaptive binarization methods were evaluated, and Niblack's method gave the best performance.
---
paper_title: Thinning methodologies−a comprehensive survey

paper_content:

A comprehensive survey of thinning methodologies is presented. A wide range of thinning algorithms, including iterative deletion of pixels and nonpixel-based methods, is covered. Skeletonization algorithms based on medial axis and other distance transforms are not considered. An overview of the iterative thinning process and the pixel-deletion criteria needed to preserve the connectivity of the image pattern is given first. Thinning algorithms are then considered in terms of these criteria and their modes of operation. Nonpixel-based methods that usually produce a center line of the pattern directly in one pass without examining all the individual pixels are discussed. The algorithms are considered in great detail and scope, and the relationships among them are explored. >
---
paper_title: Line thinning by line following

paper_content:

Abstract A line following algorithm is presented as a means to perform line thinning on elongated figures. The method is faster than conventional thinning algorithms and not as sensitive to noise. It can be useful for other applications also.
---
paper_title: A pattern adaptive thinning algorithm

paper_content:

A simple sequential thinning algorithm for peeling off pixels along contours is described. An adaptive algorithm obtained by incorporating shape adaptivity into this sequential process is also given. The distortions in the skeleton at the right-angle and acute-angle corners are minimized in the adaptive algorithm. The asymmetry of the skeleton, which is a characteristic of sequential algorithm, and is due to the presence of T-corners in some of the even-thickness pattern is eliminated. The performance (in terms of time requirements and shape preservation) is compared with that of a modern thinning algorithm.
---
paper_title: Skeletonization of Arabic characters using clustering based skeletonization algorithm (CBSA)

paper_content:

Abstract Character skeletonization is an essential step in many character recognition techniques. In this paper, skeletonization of Arabic characters is addressed. While other techniques employ thinning algorithms, in this paper clustering of Arabic characters is used. The use of clustering technique (an expensive step) is justified by the properties of the generated skeleton which has the advantages of other thinning techniques and is robust. The presented technique may be used in the modeling and training stages to reduce the processing time of the recognition system.
---
paper_title: An evaluation of parallel thinning algorithms for character recognition

paper_content:

Skeletonization algorithms have played an important role in the preprocessing phase of OCR systems. In this paper we report on the performance of 10 parallel thinning algorithms from this perspective by gathering statistics from their performance on large sets of data and examining the effects of the different thinning algorithms on an OCR system. >
---
paper_title: Fast fully parallel thinning algorithms

paper_content:

Abstract Three new fast fully parallel 2-D thinning algorithms using reduction operators with 11-pixel supports are presented and evaluated. These are compared to earlier fully parallel thinning algorithms in tests on artificial and natural images; the new algorithms produce either superior parallel computation time (number of parallel iterations) or thinner medial curve results with comparable parallel computation time. Further, estimates of the best possible parallel computation time are developed which are applied to the specific test sets used. The parallel computation times of the new algorithms and one earlier algorithm are shown to approach closely or surpass these estimates and are in this sense near optimally fast.
---
paper_title: Segmentation methods for character recognition: from segmentation to document structure analysis

paper_content:

A pattern-oriented segmentation method for optical character recognition that leads to document structure analysis is presented. As a first example, segmentation of handwritten numerals that touch are treated. Connected pattern components are extracted, and spatial interrelations between components are measured and grouped into meaningful character patterns. Stroke shapes are analyzed and a method of finding the touching positions that separates about 95% of connected numerals correctly is described. Ambiguities are handled by multiple hypotheses and verification by recognition. An extended form of pattern-oriented segmentation, tabular form recognition, is considered. Images of tabular forms are analyzed, and frames in the tabular structure are extracted. By identifying semantic relationships between label frames and data frames, information on the form can be properly recognized. >
---
paper_title: Reading handwritten words using hierarchical relaxation

paper_content:

Abstract Handwritten words are read (recognized) by applying hierarchical relaxation labeling procedures to a hierarchical description of the word. The H and w ritten W ord R eading S ystem (HWWRS) builds a hierarchical description of the word it reads. HWWRS's first task is to translate the array representation (image) of a word into a geometric graph representation that models the line structures in the word. A stroke graph containing the possible strokes in the word is created by matching structural descriptions of strokes to the geometric graph. The stroke graph is then augmented to a letter-stroke graph that represents the possible letters of the word by matching structural descriptions of letters to the stroke graph. The letter-stroke graph represents the ambiguous segmentations of the word into letters and strokes that possibly overlap one another. It constitutes the hierarchical description of the input word. The hierarchical description is contextually disambiguated and thus simplified by the application of relaxation procedures. This simplified hierarchical description defines one or possibly a few words. HWWRS reads the input word by picking the most likely word from a list of possible words. The system is shown to function correctly for several example words, and experiments involving different relaxation formulas are described.
---
paper_title: Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques

paper_content:

A lexicon-based, handwritten word recognition system combining segmentation-free and segmentation-based techniques is described. The segmentation-free technique constructs a continuous density hidden Markov model for each lexicon string. The segmentation-based technique uses dynamic programming to match word images and strings. The combination module uses differences in classifier capabilities to achieve significantly better performance.
---
paper_title: Word recognition in a segmentation-free approach to OCR

paper_content:

Segmentation is a key step in current OCR systems. It has been estimated that half the errors in character recognition are due to segmentation. A novel approach that performs OCR without the segmentation step was developed. The approach starts by extracting significant geometric features from the input document image of the page. Each feature then votes for the character that could have generated that feature. Thus, even if some of the features are occluded or lost due to degradation, the remaining features can successfully identify the character. In extreme cases, the degradation may be severe enough to prevent recognition of some of the characters in a word. In such cases, a lexicon-based word recognition technique is used to resolve ambiguity. Inexact matching and probabilistic evaluation used in the technique make it possible to identify the correct word, by detecting a partial set of characters. The authors first present an overview of their segmentation-free OCR system and then focus on the word recognition technique. Preliminary experimental results show that this is a very promising approach. >
---
paper_title: Major components of a complete text reading system

paper_content:

The document image processes used in a recently developed text reading system are described. The system consists of three major components: document analysis, document understanding, and character segmentation/recognition. The document analysis component extracts lines of text from a page for recognition. The document understanding component extracts logical relationships between the document constituents. The character segmentation/recognition component extracts characters from a text line and recognizes them. Experiments on more than a hundred documents have proved that the proposed approaches to document analysis and document understanding are robust even for multicolumned and multiarticle documents containing graphics and photographs, and that the proposed character segmentation/recognition method is robust enough to cope with omnifont characters which frequently touch each other. >
---
paper_title: The document spectrum for page layout analysis

paper_content:

Page layout analysis is a document processing technique used to determine the format of a page. This paper describes the document spectrum (or docstrum), which is a method for structural page layout analysis based on bottom-up, nearest-neighbor clustering of page components. The method yields an accurate measure of skew, within-line, and between-line spacings and locates text lines and text blocks. It is advantageous over many other methods in three main ways: independence from skew angle, independence from different text spacings, and the ability to process local regions of different text orientations within the same image. Results of the method shown for several different page formats and for randomly oriented subpages on the same image illustrate the versatility of the method. We also discuss the differences, advantages, and disadvantages of the docstrum with respect to other lay-out methods. >
---
paper_title: Document representation and its application to page decomposition

paper_content:

Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl times/3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.
---
paper_title: Structured document segmentation and representation by the modified X-Y tree

paper_content:

We describe a top-down approach to the segmentation and representation of documents containing tabular structures. Examples of these documents are invoices and technical papers with tables. The segmentation is based on an extension of X-Y trees, where the regions are split by means of cuts along separators (e.g. lines), in addition to cuts along white spaces. The leaves describe regions containing homogeneous information and cutting separators. Adjacency links among leaves of the tree describe local relationships between corresponding regions.
---
paper_title: A new scheme for off-line handwritten connected digit recognition

paper_content:

A scheme is proposed for off-line handwritten connected digit recognition, which uses a sequence of segmentation and recognition algorithms. First, the connected digits are segmented by employing both the gray scale and binary information. Then, a new set of features is extracted from the segments. The parameters of the feature set are adjusted during the training stage of the hidden Markov model (HMM) where the potential digits are recognized. Finally, in order to confirm the preliminary segmentation and recognition results, a recognition based segmentation method is presented.
---
paper_title: Off-line cursive word recognition

paper_content:

The state of the art in handwriting recognition, especially in cursive word recognition, is surveyed, and some basic notions are reviewed in the field of picture recognition, particularly, line image recognition. The usefulness of 'regular' versus 'singular' classes of features is stressed. These notions are applied to obtain a graph, G, representing a line image, and also to find an 'axis' as the regular part of G. The complements to G of the axis are the 'tarsi', singular parts of G, which correspond to informative features of a cursive word. A segmentation of the graph is obtained, giving a symbolic description chain (SDC). Using one or more as robust anchors, possible words in a list of words are selected. Candidate words are examined to see if the other letters fit the rest of the SDC. Good results are obtained for clean images of words written by several persons. >
---
paper_title: A survey of methods and strategies in character segmentation

paper_content:

Character segmentation has long been a critical area of the OCR process. The higher recognition rates for isolated characters vs. those obtained for words and connected character strings well illustrate this fact. A good part of recent progress in reading unconstrained printed and written text may be ascribed to more insightful handling of segmentation. This paper provides a review of these advances. The aim is to provide an appreciation for the range of techniques that have been developed, rather than to simply list sources. Segmentation methods are listed under four main headings. What may be termed the "classical" approach consists of methods that partition the input image into subimages, which are then classified. The operation of attempting to decompose the image into classifiable units is called "dissection." The second class of methods avoids dissection, and segments the image either explicitly, by classification of prespecified windows, or implicitly by classification of subsets of spatial features collected from the image as a whole. The third strategy is a hybrid of the first two, employing dissection together with recombination rules to define potential segments, but using classification to select from the range of admissible segmentation possibilities offered by these subimages. Finally, holistic approaches that avoid segmentation by recognizing entire character strings as units are described.
---
paper_title: Hidden Markov Models in Handwriting Recognition

paper_content:

Hidden Markov Models (HMM) have now became the prevalent paradigm in automatic speech recognition. Only recently, several researchers in off-line handwriting recognition have tried to transpose the HMM technology to their field after realizing that word images could be assimilated to sequences of observations. HMM’s form a family of tools for modelling sequential processes in a statistical and generative manner. Their reputation is due to the results attained in speech recognition which derive mostly from the existence of automatic training techniques and the advantages of the probabilistic framework. This article first reviews the basic concepts of HMM’s. The second part is devoted to illustrative applications in the field of off- line handwriting recognition. We describe four different applications of HMM’s in various contexts and review some of the other approaches.
---
paper_title: A new approach to document analysis based on modified fractal signature

paper_content:

This paper presents a new approach to document analysis. The proposed approach is based on modified fractal signature. Instead of the time-consuming traditional approaches (top-down and bottom-up approaches) where iterative operations are necessary to break a document into blocks to extract its geometric (layout) structure, this new approach can divide a document into blocks in only one step. This approach can be used to process documents with high geometrical complexity. Experiments have been conducted to prove the proposed new approach for document processing.
---
paper_title: Off-line handwritten word recognition using a hidden Markov model type stochastic network

paper_content:

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported. >
---
paper_title: Segmentation of merged characters by neural networks and shortest path

paper_content:

Abstract A major problem with a neural network-based approach to printed character recognition is the segmentation of merged characters. A hybrid method is proposed which combines a neural network-based deferred segmentation scheme with conventional immediate segmentation techniques. In the deferred segmentation, a neural network is employed to distinguish single characters from composites. To find a proper vertical cut that separates a composite, a shortest-path algorithm seeking minimal-penalty curved cuts is used. Integrating those components with a multiresolution neural network OCR and an efficient spelling checker, the resulting system significantly improves its ability to read omnifont document text.
---
paper_title: Adaptive document segmentation and geometric relation labeling: algorithms and experimental results

paper_content:

This paper describes a generic document segmentation and geometric relation labeling method with applications to document analysis. Unlike the previous document segmentation methods where text spacing, border lines, and/or a priori layout models based template processing are performed, the present method begins with a hierarchy of partitioned image layers where inhomogeneous higher-level regions are recursively positioned into lower-level rectangular subregions and at the same time lower-level smaller homogeneous regions are merged into larger homogeneous regions. The present method differs from the traditional split-and-merge segmentation method in that it orthogonally splits regions using thresholds adaptively computed from projection profiles.
---
paper_title: Page segmentation based on thinning of background

paper_content:

This paper presents a new method of page segmentation based on the analysis of background (white areas). The proposed method is capable of segmenting pages with non-rectangular layout as well as with various angles of skew. The characteristics of the method are as follows: (1) thinning of the background enables us to represent white areas of any shape as connected thin lines or chains and the robustness for tilted page images is also achieved by the representation; and (2) based on this representation, the task of page segmentation is defined as to find the loops enclosing printed areas. The task is achieved by eliminating unnecessary chains using not only a feature of white areas, but also a feature of black areas divided by a chain. Based on the experimental results and the comparison with previous methods, we discuss the advantages and limitations of the proposed method.
---
paper_title: Page segmentation by segment tracing

paper_content:

A page segmentation method that allows one to cut a document page image into polygonal blocks as well as into classical rectangular blocks is described. The intercolumn and interparagraph gaps are extracted as horizontal and vertical lines. The points of intersection between these lines are treated as vertices of polygonal blocks. With the aid of the 4-connected chain codes and an intersection table, simple isothetic polygonal blocks are constructed from these points of intersection. >
---
paper_title: Analysis of class separation and combination of class-dependent features for handwriting recognition

paper_content:

In this paper, we propose a new approach to combine multiple features in handwriting recognition based on two ideas: feature selection-based combination and class dependent features. A nonparametric method is used for feature evaluation, and the first part of this paper is devoted to the evaluation of features in terms of their class separation and recognition capabilities. In the second part, multiple feature vectors are combined to produce a new feature vector. Based on the fact that a feature has different discriminating powers for different classes, a new scheme of selecting and combining class-dependent features is proposed. In this scheme, a class is considered to have its own optimal feature vector for discriminating itself from the other classes. Using an architecture of modular neural networks as the classifier, a series of experiments were conducted on unconstrained handwritten numerals. The results indicate that the selected features are effective in separating pattern classes and the new feature vector derived from a combination of two types of such features further improves the recognition rate.
---
paper_title: Preprocessing techniques for cursive script word recognition

paper_content:

Abstract This paper deals with techniques for improving the recognition rate of a cursive script word recognition system. Closed-loop preprocessing techniques have been designed and implemented to achieve this objective on a limited vocabulary but with no restrictions on handwriting style. This paper discusses the details of such a system and its performance on samples from several authors. Results obtained from this study are promising and suggest that closed-loop verification is a potentially more useful technique than previous open-loop processing approaches.
---
paper_title: Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques

paper_content:

A lexicon-based, handwritten word recognition system combining segmentation-free and segmentation-based techniques is described. The segmentation-free technique constructs a continuous density hidden Markov model for each lexicon string. The segmentation-based technique uses dynamic programming to match word images and strings. The combination module uses differences in classifier capabilities to achieve significantly better performance.
---
paper_title: A practical pattern recognition system for translation, scale and rotation invariance

paper_content:

We present a practical pattern recognition system that is invariant with respect to translation, scale and rotation of objects. The system is also insensitive to large variations of the threshold used. As feature vectors, Zernike moments are used and we compare them with Hu's seven moment invariants. For a practical machine vision system, three key issues are discussed: pattern normalization, fast computation of Zernike moments, and classification using k-NN rule. As testing results, the system recognizes a set of 62 alphanumeric machine-printed characters with different sizes, at arbitrary orientations, and with different thresholds where the size of the characters varies from 10/spl times/10 to 512/spl times/512 pixels. >
---
paper_title: Multi-layer projections for the classification of similar Chinese characters

paper_content:

An algorithm is presented of extracting features from Chinese characters. These features consist of the Fourier spectrum of projections obtained from multiple-layers of annular partitions. This method takes into consideration the square shape of Chinese characters to that the extracted features contain the significant information of the different parts of the character, and are insensitive to rotation and linear displacement. For the experiments, 97 similar Chinese characters were selected from the most frequently used characters. These characters were divided into 34 groups according to similarity in shape. Three different fonts of Chinese characters (Song, Kai and Bold face) were used. Four additional symbols were also included to study the effects of character symmetry on the proposed algorithm. Experimental results indicate that for any displacement and for rotations in the range of (-180 degrees , +180 degrees ), this method can separate without exception all similar Chinese characters including the complex ones. >
---
paper_title: The feature extraction of Chinese character based on contour information

paper_content:

A new method, called central projection transformation, is proposed in this paper for feature extraction. From our experiments, the new method is found to be efficient in extracting features based on the contours of Chinese characters. Chinese characters have complex structures, and some of them are composed of several separate components, so several contours are embedded in a character. This may obstruct the application of the contour approach in recognizing Chinese characters. Central projection transformation can convert such a multi-contour pattern into a solid, convex pattern whose contour is a unique polygon. Most of the information of this new pattern is still located around its periphery. This approach can greatly simplify the processing of Chinese characters and other multi-contour patterns. It is also a powerful tool for processing Arabic, Japanese and other characters.
---
paper_title: Multiresolution recognition of handwritten numerals with wavelet transform and multilayer cluster neural network

paper_content:

In this paper, we propose a new scheme for multiresolution recognition of totally unconstrained handwritten numerals using wavelet transform and a simple multilayer cluster neural network. The proposed scheme consists of two stages: a feature extraction stage for extracting multiresolution features with wavelet transform, and a classification stage for classifying totally unconstrained handwritten numerals with a simple multilayer cluster neural network. In order to verify the performance of the proposed scheme, experiments with unconstrained handwritten numeral database of Concordia University of Canada, that of Electro-Technical Laboratory of Japan, and that of Electronics and Telecommunications Research Institute of Korea were performed. The error rates were 3.20%, 0.83%, and 0.75%, respectively. These results showed that the proposed scheme is very robust in terms of various writing styles and sizes.
---
paper_title: Character recognition using statistical moments

paper_content:

Abstract This paper presents a character recognition system that is implemented using a variety of statistical moments as features. These moments include Hu moment invariants, Affine moment invariants and Tsirikolias–Mertzios moments. Euclidean distance measure, cross correlation and discrimination cost were used as the classification techniques. The mean of the intraclass standard deviations of the features was used as a weighting factor during the classification process to improve recognition accuracy. The system was rigorously tested under different conditions, including using different number of training sets and documents with different fonts. It was found that Tsirikolias–Mertzios moments with weighted cross correlation classifier provided the best recognition rates.
---
paper_title: Recognition of handprinted Chinese characters using Gabor features

paper_content:

A method for handprinted Chinese character recognition based on Gabor filters is proposed. The Gabor approach to character recognition is intuitively appealing because it is inspired by a multi-channel filtering theory for processing visual information in the early stages of the human visual system. The performance of a character recognition system using Gabor features is demonstrated on the ETL-8 character set. Mental results show that the Gabor features yielded an error rate of 2.4% versus the error rate of 4.4% obtained by using a popular feature extraction method.
---
paper_title: One dimensional representation of two dimensional information for HMM based handwritten recognition

paper_content:

We introduce a new set of one-dimensional discrete, constant length features to represent two dimensional shape information for HMM (hidden Markov model), based handwritten optical character recognition problem. The proposed feature set embeds the two dimensional information into a sequence of one-dimensional codes, selected from a code book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for HMM based shape recognition schemes. The new feature set is used in a handwritten optical character recognition scheme, where a sequence of segmentation and recognition stages is employed. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of the HMM. The proposed character recognition system is tested on both a locally generated cursively handwritten data and isolated number digits of NIST database. The experimental results indicate high recognition rates.
---
paper_title: A comparative study of different classifiers for handprinted character recognition

paper_content:

In this paper, we present a comparative study of four different classifiers for isolated handprinted character recognition. These four classifiers are i) a nearest template (NT) classifier, ii) an enhanced nearest template (ENT) classifier, iii) a standard feedforward neural network (FNN) classifier, and iv) a hybrid classifier. The NT classifier is a variation of the nearest neighbor classifier which stores a small number of templates (or prototypes) and their statistics generated by a special clustering algorithm. Motivated by radial basis function networks, the ENT classifier is proposed to augment the NT classifier with an optimal transform which maps the distances generated by the NT classifier to character categories. The FNN classifier is a 3-layer (with one hidden layer) feedforward network trained using the backpropagation algorithm. The hybrid classifier combines results from the FNN and NT classifiers in an efficient way to improve the recognition accuracy with only a slight increase in computation. In this paper, we evaluate the performance of these four classifiers in terms of recognition accuracy, top 3 coverage rate, and recognition speed, using the NIST isolated lower-case alphabet database. Our experiments show that the FNN classifier outperforms the NT and ENT classifiers in all the three evaluation criteria. The hybrid classifier achieves the best recognition accuracy at a cost of little extra computation over the FNN classifier. The ENT classifier can significantly improve the recognition accuracy of the NT classifier when a small number of templates is used.
---
paper_title: Invariant pattern recognition by moment fourier descriptor

paper_content:

Abstract This paper proposes a new shape descriptor, called moment Fourier descriptor, which can describe a complex object composed by a set of closed regions. This descriptor is shown to be independent of object's translation, rotation and scaling. The essential advantage of moment Fourier descriptor is that it can be used to recognize more complex patterns than the traditional Fourier descriptors. Simulations are given to show the high performance of the proposed approach.
---
paper_title: Recognition of legal amounts on bank cheques

paper_content:

This article describes the recognition of legal amounts of a bank cheque processing system developed at CENPARMI. The preprocessing, sentence to word segmentation and word recognition approaches are presented along with some critical reviews. The overall engine is a combination of a global feature scheme with an HMM module. The global features consist of the encoding of the relative position of the ascenders, descenders and loops within a word. The HMM uses one feature set based on the orientation of contour points as well as their distance to the baselines. Our system is fully trainable, reducing to a strict minimum the number of hand-set parameters. The system is also modular and independent of specific languages as we have to deal with at least two languages in Canada, namely English and French. The system can be easily adapted to read other European languages based on the Roman alphabet. The system is continuously tested on data from the local phone company, and we report here the results on a balanced French database of approximately 2000 cheques with specified amounts.
---
paper_title: Chaincode contour processing for handwritten word recognition

paper_content:

Contour representations of binary images of handwritten words afford considerable reduction in storage requirements while providing lossless representation. On the other hand, the one-dimensional nature of contours presents interesting challenges for processing images for handwritten word recognition. Our experiments indicate that significant gains are to be realized in both speed and recognition accuracy by using a contour representation in handwriting applications.
---
paper_title: Off-line recognition of cursive script produced by a cooperative writer

paper_content:

A method for the off-line recognition of cursive handwriting based on hidden Markov models is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An average correct recognition rate of over 98% on the word level has been achieved in experiments with cooperative writers using two dictionaries of 150 words each.
---
paper_title: Structural Feature Extraction Using Multiple Bases

paper_content:

Abstract The prime difficulty in research and development of the handwritten character recognition systems is in the variety of shape deformations. In particular, throughout more than a quarter of a century of research, it is found that some qualitative features such as quasi-topological features (convexity and concavity), directional features, and singular points (branch points and crossings) are effective in coping with variations of shapes. On the basis of this observation, Nishida and Mort ( IEEE Trans. Pattern Anal. Mach. Intell. 14, 1992, 516-533; and Structured Document Image Analysis (H. S. Baird, H. Bunke, and K. Yamamoto, Eds.), pp. 139-187, Springer-Verlag, New York, 1992) proposed a method for structural description of character shapes by few components with rich features. This method is clear and rigorous, can cope with various deformations, and has been shown to be powerful in practice. Furthermore, shape prototypes (structural models) can be constructed automatically from the training data (Nishida and Mori, IEEE Trans. Pattern Anal. Mach. Intell. 15, 1993, 1298-1311). However, in the analysis of directional features, the number of directions is fixed to 4, and more directions such as 8 or 16 cannot be dealt with. For various applications of Nishida and Mori's method, we present a method for structural analysis and description of simple arcs or closed curves based on 2 m -directional features ( m = 2, 3, 4, ...) and convex/concave features. On the other band, software OCR systems without specialized hardware have attracted much attention recently. Based on the proposed method of structural analysis and description, we describe a software implementation of a handwritten character recognition system using multistage strategy.
---
paper_title: A method of extracting curvature features and its application to handwritten character recognition

paper_content:

Proposes an orthodox method of extracting curvature features based on a curve fitting approximation. It enables us to obtain analog values of curvatures of a given curve. This means that a so-called gray zone between two categories can be identified and also that very shallow concavities can be detected. For this purpose, cubic B-splines are obtained using a least squares method with natural conditions at the end-points. The method was tested on synthesized noisy data sets such as 2/spl rarr/Z, 4/spl rarr/9 and 1/spl rarr/3. The results are so good that the method can be used to obtain analog features as intended. Demonstrative experimental results are shown for the data set for 1/spl rarr/3.
---
paper_title: HMM-KNN word recognition engine for bank cheque processing

paper_content:

Describes the mixed HMM-KNN word recognition module of a bank cheque processing system developed at CENPARMI. It uses a combination of 2 segmentation free word recognition schemes. The first scheme uses a set of global features associated to a modified K nearest neighbour classifier; while the second one uses a set of directional contour features as input to an HMM. The system has been designed to be modular and independent of specific languages as in Canada one has to deal with at least 2 languages, namely English and French. It can be easily adapted to read other European languages based on the Roman alphabet. The system is continuously tested on data from the local phone company, and we report here the results on a database of approximately 4,500 cheques.
---
paper_title: On optimal order in modeling sequence of letters in words of common language as a Markov chain

paper_content:

Abstract In recognition of words of a language such as English, the letter sequences of the words are often modeled as Markov chains. In this paper the problem of determining the optimal order of such Markov chains is addressed using Tong's minimum Akaike information criterion estimate (MAICE) approach and Hoel's likelihood ratio statistic based hypothesis-testing approach. Simulation results show that the sequence of letters in English words is more likely to be a second order Markov chain than a first order one.
---
paper_title: Distance features for neural network-based recognition of handwritten characters

paper_content:

Features play an important role in OCR systems. In this paper, we propose two new features which are based on distance information. In the first feature (called DT, Distance Transformation), each white pixel has a distance value to the nearest black pixel. The second feature is called DDD (Directional Distance Distribution) which contains rich information encoding both the black/white and directional distance distributions. A new concept of map tiling is introduced and applied to the DDD feature to improve its discriminative power. For an objective evaluation and comparison of the proposed and conventional features, three distinct sets of characters (i.e., numerals, English capital letters, and Hangul initial sounds) have been tested using standard databases. Based on the results, three propositions can be derived to confirm the superiority of both the DDD feature and the map tilings.
---
paper_title: On-line handwriting character recognition method with directional features and direction-change features

paper_content:

We propose a new on-line recognition method to recognize handwritten cursive-style characters correctly. Our method simultaneously uses both directional features, otherwise known as off-line features, and direction-change features, which we designed as on-line features. These features are expressed in the divided meshes of the character area. The directional features express the directions between character's coordinates within the meshes. The direction-change features express where in the mesh and in which direction each direction of the character's coordinates change, and express where the circular parts of the character are in the mesh. These features express both written strokes in the pen-down state and unwritten imaginary strokes in the pen-up state. Our method recognizes an inputted character by comparing the inputted character's features and the features of standard characters. The recognition rate was improved by our method with directional features and direction-change features as opposed to the traditional method with only directional features. Moreover, the recognition rate was also improved by considering imaginary strokes in the pen-up state.
---
paper_title: Recognition of handprinted chinese characters via stroke relaxation

paper_content:

Abstract A new relaxation matching method based on the information of the neighborhood relationship among extracted sub-strokes is proposed to recognize handprinted Chinese characters (HCCs). In order to ensure the convergence in the relaxation process, a new iterated scheme is devised. A supporting function is also designed to solve the problem of wide variability among writers and some inevitable defects in the preprocessing procedure. The distance function on which the matching possibilities of sub-strokes are reflected is determined by using the linear programming method to obtain the best result. The experiments are conducted by using the Kanji of the ETL-8 database. From the experimental results, it is shown that the proposed algorithm does improve the recognition rate of HCCs.
---
paper_title: Direct extraction of topographic features for gray scale character recognition

paper_content:

Optical character recognition (OCR) traditionally applies to binary-valued imagery although text is always scanned and stored in gray scale. However, binarization of multivalued image may remove important topological information from characters and introduce noise to character background. In order to avoid this problem, it is indispensable to develop a method which can minimize the information loss due to binarization by extracting features directly from gray scale character images. In this paper, we propose a new method for the direct extraction of topographic features from gray scale character images. By comparing the proposed method with Wang and Pavlidis' method, we realized that the proposed method enhanced the performance of topographic feature extraction by computing the directions of principal curvature efficiently and prevented the extraction of unnecessary features. We also show that the proposed method is very effective for gray scale skeletonization compared to Levi and Montanari's method. >
---
paper_title: The HOVER system for rapid holistic verification of off-line handwritten phrases

paper_content:

The authors describe ongoing research on a system for rapid verification of unconstrained off-line handwritten phrases using perceptual holistic features of the handwritten phrase image. The system is used to verify handwritten street names automatically extracted from live US mail against recognition results of analytical classifiers. The system rejects errors with 98% accuracy at the 30% accept level, while consuming approximately 20 msec per image on the average on a 150 MHz SPARC 10.
---
paper_title: Hierarchical attributed graph representation and recognition of handwritten chinese characters

paper_content:

Abstract This paper presents a new method of recognizing handwritten Chinese characters. A structural representation called hierarchical attributed graph representation (HAGR) is introduced to describe handwritten Chinese characters. The HAGR provides a simple and direct representation of handwritten Chinese characters. With HAGR, the recognition process becomes a simple task of graph matching. A cost function mapping a candidate to a model graph is introduced. This approach can tolerate the variations of HAGR which reflect the instabilities or variabilities of handwritten Chinese characters resulting from different writing styles. Several rules have been introduced to rearrange the order of the vertices of the graphs in order to avoid the combinatorial explosion in graph matching. In addition, the database of the character models is organized in a search-tree structure. For a candidate character, the search process to find a corresponding model character has been divided into a number of simple and local decisions at different levels of the tree. This considerably improves the efficiency and accuracy of the matching process.
---
paper_title: Performance comparison of several feature selection methods based on node pruning in handwritten character recognition

paper_content:

The paper presents a performance comparison of several feature selection methods based on neural network node pruning. Assuming the features are extracted and presented as the inputs of a 3 layered perceptron classifier, we apply the five feature selection methods before/during/after neural network training in order to prune only input nodes of the neural network. Four of them are node pruning methods such as node saliency method, node sensitivity method, and two interactive pruning methods using different contribution measures. The last one is a statistical method based on principle component analysis (PCA). The first two of them prune input nodes during training whereas the last three do before/after network training. For gradient and upper down, left right hole concavity features, we perform several experiments of handwritten English alphabet and digit recognition with/without pruning using the five feature selection algorithms, respectively. The experimental results show that node saliency method outperforms the others.
---
paper_title: Recognizing components of handwritten characters by attributed relational graphs with stable features

paper_content:

We present a method for Chinese character component recognition. We use an attributed relational graph model to describe a component. This model allows us to express the knowledge of the component shape and can include stable features of the component in various writing styles and various characters. We then describe a method to extract graph representation of an input character. The graph models are used to recognize components from a whole character by subgraph isomorphism. Our method need not segment component from character and can tolerate links and overlaps between a component and the other part. Experiments for handwritten Chinese characters show its efficiency.
---
paper_title: Automatic feature generation for handwritten digit recognition

paper_content:

An automatic feature generation method for handwritten digit recognition is described. Two different evaluation measures, orthogonality and information, are used to guide the search for features. The features are used in a backpropagation trained neural network. Classification rates compare favorably with results published in a survey of high-performance handwritten digit recognition systems. This classifier is combined with several other high performance classifiers. Recognition rates of around 98% are obtained using two classifiers on a test set with 1000 digits per class.
---
paper_title: Statistical Pattern Recognition: A Review

paper_content:

The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
---
paper_title: Representation and Recognition of Handwritten Digits Using Deformable Templates

paper_content:

We investigate the application of deformable templates to recognition of handprinted digits. Two characters are matched by deforming the contour of one to fit the edge strengths of the other, and a dissimilarity measure is derived from the amount of deformation needed, the goodness of fit of the edges, and the interior overlap between the deformed shapes. Classification using the minimum dissimilarity results in recognition rates up to 99.25 percent on a 2,000 character subset of NIST Special Database 1. Additional experiments on an independent test data were done to demonstrate the robustness of this method. Multidimensional scaling is also applied to the 2,000/spl times/2,000 proximity matrix, using the dissimilarity measure as a distance, to embed the patterns as points in low-dimensional spaces. A nearest neighbor classifier is applied to the resulting pattern matrices. The classification accuracies obtained in the derived feature space demonstrate that there does exist a good low-dimensional representation space. Methods to reduce the computational requirements, the primary limiting factor of this method, are discussed.
---
paper_title: Handwritten word recognition using segmentation-free hidden Markov modeling and segmentation-based dynamic programming techniques

paper_content:

A lexicon-based, handwritten word recognition system combining segmentation-free and segmentation-based techniques is described. The segmentation-free technique constructs a continuous density hidden Markov model for each lexicon string. The segmentation-based technique uses dynamic programming to match word images and strings. The combination module uses differences in classifier capabilities to achieve significantly better performance.
---
paper_title: On machine recognition of hand-printed Chinese characters by feature relaxation

paper_content:

Abstract A new relaxation matching method based on features is introduced for the recognition of hand-printed Chinese characters. The types of features are selected carefully to reflect the structural information of characters. Matching probabilities between two features, one from the mask and the other from input, are computed by the relaxation method. A new distance measure between two characters based on these matching probabilities is defined. We demonstrate, through examples, the utility of the new approach in the recognition of hand-printed Chinese characters. It is especially powerful in distinguishing similarly-shaped characters within a cluster produced by preclassification.
---
paper_title: Handprinted character recognition based on spatial topology distance measurement

paper_content:

In this work we present a self-organization matching approach to accomplish the recognition of handprinted characters drawn with thick strokes. This approach is used to flex the unknown handprinted character toward matching its object characters gradually. The extracted character features used in the self-organization matching are center loci, orientation, and major axes of ellipses which fit the inked area of the patterns. Simulations provide encouraging results using the proposed method.
---
paper_title: A note on binary template matching

paper_content:

Abstract This paper considers some generalizations of binary template matching procedures which enable one to weight matches according to both statistical and spatial information.
---
paper_title: A tutorial on hidden Markov models and selected applications in speech recognition

paper_content:

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
---
paper_title: Recognition of cursive writing on personal checks

paper_content:

The system described in this paper applies Hidden Markov technology both to the task of recognizing the cursive legal amount on personal checks and the isolated (numeric) courtesy amount.
---
paper_title: A new scheme for off-line handwritten connected digit recognition

paper_content:

A scheme is proposed for off-line handwritten connected digit recognition, which uses a sequence of segmentation and recognition algorithms. First, the connected digits are segmented by employing both the gray scale and binary information. Then, a new set of features is extracted from the segments. The parameters of the feature set are adjusted during the training stage of the hidden Markov model (HMM) where the potential digits are recognized. Finally, in order to confirm the preliminary segmentation and recognition results, a recognition based segmentation method is presented.
---
paper_title: Recognition of handwritten digits using template and model matching

paper_content:

Abstract A pipeline strategy for handwritten numeral recognition that combines a two-stage template-based technique and a model-based technique is described. The template matcher combines multiple information sources. The second stage of the template matcher was trained on rejects from the first stage. The template matcher classifies 70–80% of the digits with reliability rates over 99%. It also generates class membership hypotheses for the remaining digits which constrain the model-based system. Recognition rates of 94.03–96.39% and error rates of 0.54%–1.05% are obtained on test data consisting of over 13,000 well-segmented digits from ZIP codes in the USPS mail.
---
paper_title: Noise, histogram and cluster validity for Gaussian-mixtured data

paper_content:

Abstract In this study, a critique of the clustering methodology is carried out for the definition of a cluster, determination of the number of clusters and evaluation of heuristic partitional clustering algorithms, when the data is a noisy Gaussian Mixture. The effects of noise in determining the number of clusters and the clustering parameters are investigated. Two cluster validity criteria, namely, the likelihood information criterion and the sum of squared error are described. It is concluded that these criteria can be used as a guide in deciding on the number of valid clusters. By using the proposed sum of squared error criterion, an improvement algorithm which reduces the effect of noise on the results of heuristic clustering algorithms is described.
---
paper_title: One dimensional representation of two dimensional information for HMM based handwritten recognition

paper_content:

We introduce a new set of one-dimensional discrete, constant length features to represent two dimensional shape information for HMM (hidden Markov model), based handwritten optical character recognition problem. The proposed feature set embeds the two dimensional information into a sequence of one-dimensional codes, selected from a code book. It provides a consistent normalization among distinct classes of shapes, which is very convenient for HMM based shape recognition schemes. The new feature set is used in a handwritten optical character recognition scheme, where a sequence of segmentation and recognition stages is employed. The normalization parameters, which maximize the recognition rate, are dynamically estimated in the training stage of the HMM. The proposed character recognition system is tested on both a locally generated cursively handwritten data and isolated number digits of NIST database. The experimental results indicate high recognition rates.
---
paper_title: Cursive script recognition by elastic matching

paper_content:

Dynamic programming has been found useful for performing nonlinear time warping for matching patterns in automatic speech recognition. Here, this technique is applied to the problem of recognizing cursive script. The parameters used in the matching are derived from time sequences of x-y coordinate data of words handwritten on an electronic tablet. Chosen for their properties of invariance with respect to size and translation of the writing, these parameters are found particularly suitable for the elastic matching technique. A salient feature of the recognition system is the establishment, in a training procedure, of prototypes by each writer using the system. In this manner, the system is tailored to the user. Processing is performed on a word-by-word basis after the writing is separated into words. Using prototypes for each letter, the matching procedure allows any letter to follow any letter and finds the letter sequence which best fits the unknown word. A major advantage of this procedure is that it combines letter segmentation and recognition in one operation by, in essence, evaluating recognition at all possible segmentations, thus avoiding the usual segmentation-then-recognition philosophy. Results on cursive writing are presented where the alphabet is restricted to the lower-case letters. Letter recognition accuracy is over 95 percent for each of three writers.
---
paper_title: Handwritten character classification using nearest neighbor in large databases

paper_content:

Shows that systems built on a simple statistical technique and a large training database can be automatically optimized to produce classification accuracies of 99% in the domain of handwritten digits. It is also shown that the performance of these systems scale consistently with the size of the training database, where the error rate is cut by more than half for every tenfold increase in the size of the training set from 10 to 100,000 examples. Three distance metrics for the standard nearest neighbor classification system are investigated: a simple Hamming distance metric, a pixel distance metric, and a metric based on the extraction of penstroke features. Systems employing these metrics were trained and tested on a standard, publicly available, database of nearly 225,000 digits provided by the National Institute of Standards and Technology. Additionally, a confidence metric is both introduced by the authors and also discovered and optimized by the system. The new confidence measure proves to be superior to the commonly used nearest neighbor distance. >
---
paper_title: A Hierarchical Approach to Efficient Curvilinear Object Searching

paper_content:

Curvilinear object searching is a common problem encountered in pattern recognition and information retrieval. How to improve the efficiency of searching is the major concern, especially when the data set is large. In this paper we propose a hierarchical approach, where high-level, salient shape features of various types are extracted and used to represent curvilinear objects at different levels of abstraction. The searching process is carried out top-down?first at the top level where only numbers of features of the same type are compared, then at the middle level where the geometric constraints among the features are checked, and finally at the bottom level where the parts between the features are considered. The searching space is reduced at each level and finally the most extensive matching operation needs to be applied to only a restricted set of candidates, thus achieving high efficiency. The general scheme has been implemented in two different applications, road image matching and cursive handwriting recognition. Experimental results from both applications are reported. Guidelines for feature selection are also provided to facilitate adaptation of the general scheme to other applications.
---
paper_title: Generalized hidden Markov models. II. Application to handwritten word recognition

paper_content:

For part I see ibid. vol.8, no. 1 (2000). This paper presents an application of the generalized hidden Markov models to handwritten word recognition. The system represents a word image as an ordered list of observation vectors by encoding features computed from each column in the given word image. Word models are formed by concatenating the state chains of the constituent character hidden Markov models. The novel work presented includes the preprocessing, feature extraction, and the application of the generalized hidden Markov models to handwritten word recognition. Methods for training the classical and generalized (fuzzy) models are described. Experiments were performed on a standard data set of handwritten word images obtained from the US Post Office mail stream, which contains real-word samples of different styles and qualities.
---
paper_title: Techniques for automatically correcting words in text

paper_content:

Research aimed at correcting words in text has focused on three progressively more difficult problems:(1) nonword error detection; (2) isolated-word error correction; and (3) context-dependent work correction. In response to the first problem, efficient pattern-matching and n -gram analysis techniques have been developed for detecting strings that do not appear in a given word list. In response to the second problem, a variety of general and application-specific spelling correction techniques have been developed. Some of them were based on detailed studies of spelling error patterns. In response to the third problem, a few experiments using natural-language-processing tools or statistical-language models have been carried out. This article surveys documented findings on spelling error patterns, provides descriptions of various nonword detection and isolated-word error correction techniques, reviews the state of the art of context-dependent word correction techniques, and discusses research issues related to all three areas of automatic error correction in text.
---
paper_title: Off-line handwritten word recognition using a hidden Markov model type stochastic network

paper_content:

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported. >
---
paper_title: The segmental K-means algorithm for estimating parameters of hidden Markov models

paper_content:

The authors discuss and document a parameter estimation algorithm for data sequence modeling involving hidden Markov models. The algorithm, called the segmental K-means method, uses the state-optimized joint likelihood for the observation data and the underlying Markovian state sequence as the objective function for estimation. The authors prove the convergence of the algorithm and compare it with the traditional Baum-Welch reestimation method. They also print out the increased flexibility this algorithm offers in the general speech modeling framework. >
---
paper_title: An HMM-based legal amount field OCR system for checks

paper_content:

The system described in this paper applies hidden Markov technology to the task of recognizing the handwritten legal amount on personal checks. We argue that the most significant source of error in handwriting recognition is the segmentation process. In traditional handwriting OCR systems, recognition is performed at the character level, using the output of an independent segmentation step. Using a fixed stepsize series of vertical slices from the image, the HMM system described in this paper avoids taking segmentation decisions early in the recognition process.
---
paper_title: Fuzzy approach to solve the recognition problem of handwritten chinese characters

paper_content:

Abstract A method based on the concept of fuzzy set for handwritten Chinese character (HCC) recognition is proposed in this paper. Chinese characters can be viewed as a collection of line segments, called strokes. Since the strokes under consideration here are fuzzy in nature, the concept of fuzzy set is utilized in the similarity measure. Two membership functions are defined for the location measure and type measure between two strokes, and a function of fuzzy entropy is used in information measure. Although the recognition problem can be reduced to the assignment problem, some modifications are still necessary. All the similarities between the corresponding strokes can be chosen by solving the assignment problem using the cost function of fuzzy entropy, and then are averaged to derive the score of similarity between two Chinese characters. 881 classes of Chinese characters in ETL-8 (160 variations/class) are used as the test patterns, and the recognition rate is about 96%. In addition, experiments about the effects of the membership function based on the class separability are also discussed in this paper.
---
paper_title: A fuzzy approach to hand-written rotation-invariant character recognition

paper_content:

A novel approach based on fuzzy set theory is developed for recognizing handwritten rotated characters. This fuzzy approach consists of four steps: (1) generating crisp sets for reference characters rotated through different degrees; (2) fuzzifying these crisp sets; (3) determining the degrees of a given character to the fuzzy sets; and (4) classifying the given character based on an average rule or a maximum rule. Simulation results show that the fuzzy approach correctly classified 94% to 100% of a small test set of characters. >
---
paper_title: A fuzzy-syntactic approach to allograph modeling for cursive script recognition

paper_content:

This paper presents an original method for creating allograph models and recognizing them within cursive handwriting. This method concentrates on the morphological aspect of cursive script recognition. It uses fuzzy-shape grammars to define the morphological characteristics of conventional allographs which can be viewed as basic knowledge for developing a writer independent recognition system. The system uses no linguistic knowledge to output character sequences that possibly correspond to an unknown cursive word input. The recognition method is tested using multi-writer cursive random letter sequences. For a test dataset containing a handwritten cursive text 600 characters in length written by ten different writers, average character recognition rates of 84.4% to 91.6% are obtained, depending on whether only the best character sequence output of the system is considered or if the best of the top 10 is accepted. These results are achieved without any writer-dependent tuning. The same dataset is used to evaluate the performance of human readers. An average recognition rate of 96.0% was reached, using ten different readers, presented with randomized samples of each writer. The worst reader-writer performance was 78.3%. Moreover, results show that system performances are highly correlated with human performances. >
---
paper_title: A fuzzy graph theoretic approach to recognize the totally unconstrained handwritten numerals

paper_content:

Abstract An automatic off-line character recognition system for totally unconstrained handwritten numerals is presented. The system was trained and tested on the field data collected by the U.S. Postal Services Department from dead letter envelopes. It was trained on 1763 unnormalized samples. The training process produced a feasible set of 105 Fuzzy Constrained Character Graph Models (FCCGMs). FCCGMs tolerate large variability in size, shape and writing style. Characters were recognized by applying a set of rules to match a character tree representation to a FCCGM. A character tree is obtained by first converting the character skeleton into an approximate polygon and then transforming the polygon into a tree structure suitable for recognition purposes. The system was tested on (not including the training set) 1812 unnormalized samples and it proved to be powerful in recognition rate and tolerance to multi-writer, multi-pen, multi-textured paper, and multi-color ink. Reliability, recognition, substitution error, and rejection rates of the system are 97.1, 90.7, 2.9, and 6.4%, respectively.
---
paper_title: Dynamic-programming-based handwritten word recognition using the Choquet fuzzy integral as the match function

paper_content:

The Choquet fuzzy integral is applied to handwritten ::: word recognition. A handwritten word recognition system is described. The word recognition system assigns a recognition confidence value to each string in a lexicon of candidate strings. The system uses a lexicon-driven approach that integrates segmentation and recognition via dynamic programming matching. The dynamic programming matcher finds a segmentation of the word image for each string in the lexicon. The traditional match score between a segmentation and a string is an average. In this paper, fuzzy integrals are used instead of an average. Experimental results demonstrate the utility of this approach. A surprising result is obtained that indicates a simple choice of fuzzy integral works better than a more complex choice.
---
paper_title: Alternatives to variable duration HMM in handwriting recognition

paper_content:

A successful handwritten word recognition (HWR) system using a variable duration hidden Markov model (VDHMM) and the path discriminant-HMM (PD-HMM) strategy is easy to implement. The central theme of the paper is to show that if the duration statistics are computed, it could be utilized to implement a model-discriminant-HMM (MD-HMM) approach for better experimental results. The paper also describes a PD-HMM based HWR system where the duration statistics are not explicitly computed, but results are still comparable to VDHMM based HWR scheme.
---
paper_title: Off-line handwritten word recognition using a hidden Markov model type stochastic network

paper_content:

Because of large variations involved in handwritten words, the recognition problem is very difficult. Hidden Markov models (HMM) have been widely and successfully used in speech processing and recognition. Recently HMM has also been used with some success in recognizing handwritten words with presegmented letters. In this paper, a complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model type stochastic network is presented. Our scheme includes a morphology and heuristics based segmentation algorithm, a training algorithm that can adapt itself with the changing dictionary, and a modified Viterbi algorithm which searches for the (l+1)th globally best path based on the previous l best paths. Detailed experiments are carried out and successful recognition results are reported. >
---
paper_title: Recognition of printed text under realistic conditions

paper_content:

Abstract Past research in OCR has focused on the shape analysis of binarized images, quite often assuming good quality document and isolated characters. Such assumptions are challenged by the conditions met in practice: binarization is difficult for low contrast documents, characters often touch each other, not only on the sides but also between lines, etc. After a brief review of past work we will describe current efforts to deal with OCR as a signal processing problem where the causes of noise and distortions as well the idealized images (definitions of typefaces) are modeled and subjected to a quantitative analysis. The key idea of the analysis is that while printed text images may be binary in an ideal state, the images seen by the sensors are gray scale because of convolution distortion and other causes. Therefore binarization should be carried out at the same time as feature extraction.
---
paper_title: A Syntactic Approach for Handwritten Mathematical Formula Recognition

paper_content:

Mathematical formulas are good examples of two-dimensional patterns as well as pictures or graphics. The use of syntactic methods is useful for interpreting such complex patterns. In this paper we propose a system for the interpretation of 2-D mathematic formulas based on a syntactic parser. This system is able to recognize a large class of 2-D mathematic formulas written on a graphic tablet. It starts the parsing by localization of the ``principal'' operator in the formula and attempts to partition it into subexpressions which are similarly analyzed by looking for a starting character. The generalized parser used in the system has been developed in our group for continuous speech recognition and picture interpretation.
---
paper_title: A high-accuracy syntactic recognition algorithm for handwritten numerals

paper_content:

A new set of topological features (primitives) for use with a syntactic classifier for high-accuracy recognition of handwritten numerals is proposed. The tree grammar used in this study makes it possible to achieve high-recognition speeds with minimal preprocessing of the test pattern.
---
paper_title: Character recognition without segmentation

paper_content:

A segmentation-free approach to OCR is presented as part of a knowledge-based word interpretation model. It is based on the recognition of subgraphs homeomorphic to previously defined prototypes of characters. Gaps are identified as potential parts of characters by implementing a variant of the notion of relative neighborhood used in computational perception. Each subgraph of strokes that matches a previously defined character prototype is recognized anywhere in the word even if it corresponds to a broken character or to a character touching another one. The characters are detected in the order defined by the matching quality. Each subgraph that is recognized is introduced as a node in a directed net that compiles different alternatives of interpretation of the features in the feature graph. A path in the net represents a consistent succession of characters. A final search for the optimal path under certain criteria gives the best interpretation of the word features. Broken characters are recognized by looking for gaps between features that may be interpreted as part of a character. Touching characters are recognized because the matching allows nonmatched adjacent strokes. The recognition results for over 24,000 printed numeral characters belonging to a USPS database and on some hand-printed words confirmed the method's high robustness level. >
---
paper_title: Recognition of handprinted chinese characters via stroke relaxation

paper_content:

Abstract A new relaxation matching method based on the information of the neighborhood relationship among extracted sub-strokes is proposed to recognize handprinted Chinese characters (HCCs). In order to ensure the convergence in the relaxation process, a new iterated scheme is devised. A supporting function is also designed to solve the problem of wide variability among writers and some inevitable defects in the preprocessing procedure. The distance function on which the matching possibilities of sub-strokes are reflected is determined by using the linear programming method to obtain the best result. The experiments are conducted by using the Kanji of the ETL-8 database. From the experimental results, it is shown that the proposed algorithm does improve the recognition rate of HCCs.
---
paper_title: Attributed Grammar-A Tool for Combining Syntactic and Statistical Approaches to Pattern Recognition

paper_content:

Attributed grammars are defned from the pattern recognidon point of view and shown to be useful for descriptions of syntactic stuctures as well as semantic attributes in primitives, subpatterns, and patterns. A pattern analysis system using attributed grammars Is proposed for pattern classification and description. This system extracts primitives and their attributes after preprocessing, performs syntax analysis of the resulting pattern representations, computes and extracts subpattern attributes for syntactically accepted patterns, and finally makes decisions according to the Bayes decision rule. Such a system uses a combination of syntactic and statistical pattern recognition techniques, as is demonstrated by illustrative examples and experimental results.
---
paper_title: On machine recognition of hand-printed Chinese characters by feature relaxation

paper_content:

Abstract A new relaxation matching method based on features is introduced for the recognition of hand-printed Chinese characters. The types of features are selected carefully to reflect the structural information of characters. Matching probabilities between two features, one from the mask and the other from input, are computed by the relaxation method. A new distance measure between two characters based on these matching probabilities is defined. We demonstrate, through examples, the utility of the new approach in the recognition of hand-printed Chinese characters. It is especially powerful in distinguishing similarly-shaped characters within a cluster produced by preclassification.
---
paper_title: Hierarchical attributed graph representation and recognition of handwritten chinese characters

paper_content:

Abstract This paper presents a new method of recognizing handwritten Chinese characters. A structural representation called hierarchical attributed graph representation (HAGR) is introduced to describe handwritten Chinese characters. The HAGR provides a simple and direct representation of handwritten Chinese characters. With HAGR, the recognition process becomes a simple task of graph matching. A cost function mapping a candidate to a model graph is introduced. This approach can tolerate the variations of HAGR which reflect the instabilities or variabilities of handwritten Chinese characters resulting from different writing styles. Several rules have been introduced to rearrange the order of the vertices of the graphs in order to avoid the combinatorial explosion in graph matching. In addition, the database of the character models is organized in a search-tree structure. For a candidate character, the search process to find a corresponding model character has been divided into a number of simple and local decisions at different levels of the tree. This considerably improves the efficiency and accuracy of the matching process.
---
paper_title: Structure recognition methods for various types of documents

paper_content:

In this paper, we describe experimental methods of recognizing the document structures of various types of documents in the framework of document understanding. Namely, we interpret document structures with individually characterized document knowledge. The document understanding process is divided into three procedures: the first is the recognition of document structures from a two-dimensional point of view; the second is the recognition of item relationships from a one-dimensional point of view; and the third is the recognition of characters from a zero-dimensional point of view. The procedure for recognizing structures plays the most important role in document understanding. This procedure extracts and classifies the logical item blocks from paper-based documents distinctly.
---
paper_title: A generalized knowledge-based system for the recognition of unconstrained handwritten numerals

paper_content:

A method of recognizing unconstrained handwritten numerals using a knowledge base is proposed. Features are collected from a training set and stored in a knowledge base that is used in the recognition stage. Recognition is accomplished by either an inference process or a structural method. The scheme is general, flexible, and applicable to different methods of feature extraction and recognition. By changing the acceptance parameters, a continuous range of performance can be achieved. Encouraging results on nearly 17000 totally unconstrained handwritten numerals are presented. The performance of the system under different recognition-rejection tradeoff ratios is analyzed in detail. >
---
paper_title: Off-line cursive word recognition

paper_content:

The state of the art in handwriting recognition, especially in cursive word recognition, is surveyed, and some basic notions are reviewed in the field of picture recognition, particularly, line image recognition. The usefulness of 'regular' versus 'singular' classes of features is stressed. These notions are applied to obtain a graph, G, representing a line image, and also to find an 'axis' as the regular part of G. The complements to G of the axis are the 'tarsi', singular parts of G, which correspond to informative features of a cursive word. A segmentation of the graph is obtained, giving a symbolic description chain (SDC). Using one or more as robust anchors, possible words in a list of words are selected. Candidate words are examined to see if the other letters fit the rest of the SDC. Good results are obtained for clean images of words written by several persons. >
---
paper_title: Techniques for automatically correcting words in text

paper_content:

Research aimed at correcting words in text has focused on three progressively more difficult problems:(1) nonword error detection; (2) isolated-word error correction; and (3) context-dependent work correction. In response to the first problem, efficient pattern-matching and n -gram analysis techniques have been developed for detecting strings that do not appear in a given word list. In response to the second problem, a variety of general and application-specific spelling correction techniques have been developed. Some of them were based on detailed studies of spelling error patterns. In response to the third problem, a few experiments using natural-language-processing tools or statistical-language models have been carried out. This article surveys documented findings on spelling error patterns, provides descriptions of various nonword detection and isolated-word error correction techniques, reviews the state of the art of context-dependent word correction techniques, and discusses research issues related to all three areas of automatic error correction in text.
---
paper_title: Building bilingual microcomputer systems

paper_content:

In the Arab world the need for bilingual microcomputer systems is ever increasing. In addition to the ability to process the Arabic and English scripts, an ideal system should support the use of existing applications with Arabic data and the access to the system facilities through Arabic interfaces. The Integrated Arabic System (IAS) was developed to study the feasibility of building such systems using existing microcomputers and software solutions.
---
paper_title: Postprocessing of recognized strings using nonstationary Markovian models

paper_content:

This paper presents nonstationary Markovian models and their application to recognition of strings of tokens. Domain specific knowledge is brought to bear on the application of recognizing zip codes in the US mailstream by the use of postal directory files. These files provide a wealth of information on the delivery points (mailstops) corresponding to each zip code. This data feeds into the models as n-grams, statistics that are integrated with recognition scores of digit images. An especially interesting facet of the model is its ability to excite and inhibit certain positions in the n-grams leading to the familiar area of Markov random fields. We empirically illustrate the success of Markovian modeling in postprocessing applications of string recognition. We present the recognition accuracy of the different models on a set of 20000 zip codes. The performance is superior to the present system which ignores all contextual information and simply relies on the recognition scores of the digit recognizers.
---
paper_title: A three-dimensional neural network model for unconstrained handwritten numeral recognition : A new approach

paper_content:

The paper describes a three-dimensional (3-D) neural network recognition system for conflict resolution in recognition of unconstrained handwritten numerals. This neural network classifier is a combination of modified self-organizing map (MSOM) and learning vector quantization (LVQ). The 3-D neural network recognition system has many layers of such neural network classifiers and the number of layers forms the third dimension. The Experiments are conducted employing SOM, MSOM, SOM and LVQ, and MSOM and LVQ networks. These experiments on a database of unconstrained handwritten samples show that the combination of MSOM and LVQ performs better than other networks in terms of classification, recognition and training time. The 3-D neural network eliminates the substitution error.
---
paper_title: Artificial Neural Networks: A Tutorial

paper_content:

Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model. It outlines network architectures and learning processes, and presents some of the most commonly used ANN models. It concludes with character recognition, a successful ANN application.
---
paper_title: Handprinted character recognition based on spatial topology distance measurement

paper_content:

In this work we present a self-organization matching approach to accomplish the recognition of handprinted characters drawn with thick strokes. This approach is used to flex the unknown handprinted character toward matching its object characters gradually. The extracted character features used in the self-organization matching are center loci, orientation, and major axes of ellipses which fit the inked area of the patterns. Simulations provide encouraging results using the proposed method.
---
paper_title: Handwritten digit recognition by adaptive-subspace self-organizing map (ASSOM)

paper_content:

The adaptive-subspace self-organizing map (ASSOM) proposed by Kohonen is a recent development in self-organizing map (SOM) computation. In this paper, we propose a method to realize ASSOM using a neural learning algorithm in nonlinear autoencoder networks. Our method has the advantage of numerical stability. We have applied our ASSOM model to build a modular classification system for handwritten digit recognition. Ten ASSOM modules are used to capture different features in the ten classes of digits. When a test digit is presented to all the modules, each module provides a reconstructed pattern and the system outputs a class label by comparing the ten reconstruction errors. Our experiments show promising results. For relatively small size modules, the classification accuracy reaches 99.3% on the training set and over 97% on the testing set.
---
paper_title: High accuracy optical character recognition using neural networks with centroid dithering

paper_content:

Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set. >
---
paper_title: Perceptrons An Introduction To Computational Geometry

paper_content:

Thank you very much for downloading perceptrons an introduction to computational geometry. As you may know, people have search numerous times for their chosen books like this perceptrons an introduction to computational geometry, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful virus inside their laptop.
---
paper_title: A novel feature recognition neural network and its application to character recognition

paper_content:

Presents a feature recognition network for pattern recognition that learns the patterns by remembering their different segments. The base algorithm for this network is a Boolean net algorithm that the authors developed during past research. Simulation results show that the network can recognize patterns after significant noise, deformation, translation and even scaling. The network is compared to existing popular networks used for the same purpose, especially the Neocognitron. The network is also analyzed as regards to interconnection complexity and information storage/retrieval. >
---
paper_title: A two-stage multi-network OCR system with a soft pre-classifier and a network selector

paper_content:

We propose a generic two-stage multi-network classification scheme and a realization of this generic scheme: a two-stage multi-network OCR system. The generic two-stage multi-network classification scheme decomposes the estimation of a posteriori probabilities into two coarse-to-fine stages. This generic classification scheme is especially suitable for the classification tasks which involve a large number of categories. The two-stage multi-network OCR system consists of a bank of specialized networks, each of which is designed to recognize a subset of whole character set. A soft pre-classifier and a network selector are employed in the two-stage multi-network OCR system for selectively invoking necessary specialized network. The network selector makes decisions based on both the prior case information and the outputs of the pre-classifier. Compared with the system which uses either a single network or one-stage multiple networks, the two-stage multi-network OCR system offers advantages in recognition accuracy, confidence measure, speed, and flexibility.
---
paper_title: A method of combining multiple experts for the recognition of unconstrained handwritten numerals

paper_content:

For pattern recognition, when a single classifier cannot provide a decision which is 100 percent correct, multiple classifiers should be able to achieve higher accuracy. This is because group decisions are generally better than any individual's. Based on this concept, a method called the "Behavior-Knowledge Space Method" was developed, which can aggregate the decisions obtained from individual classifiers and derive the best final decisions from the statistical point of view. Experiments on 46451 samples of unconstrained handwritten numerals have shown that this method achieves very promising performances and outperforms voting, Bayesian, and Dempster-Shafer approaches. >
---
paper_title: Adaptive Mixtures of Local Experts

paper_content:

We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link between these two apparently different approaches. We demonstrate that the learning procedure divides up a vowel discrimination task into appropriate subtasks, each of which can be solved by a very simple expert network.
---
paper_title: Automatic allograph selection and multiple expert classification for totally unconstrained handwritten character recognition

paper_content:

We introduce a new method for online character recognition based on the co-operation of two classifiers, respectively operating on static and dynamic character properties. Both classifiers use the nearest neighbour algorithm. References have been selected previously using an unsupervised clustering technique for selecting, in each character class, the most representative allographs. Several co-operation architectures are presented, from the easier (balanced sum of both classifier outputs) types to the most complicated (integrating neural network) one. The recognition improvement varies between 30% and 50% according to the merging technique implemented. We evaluate the performance of each method based on the recognition rate and speed. Results are presented on 62 different character classes, and more than 75000 examples are from the UNIPEN database.
---
paper_title: Recognition of handwritten numerals by Quantum Neural Network with fuzzy features

paper_content:

This paper describes a new kind of neural network – Quantum Neural Network (QNN) – and its application to the recognition of handwritten numerals. QNN combines the advantages of neural modelling and fuzzy theoretic principles. Novel experiments have been designed for in-depth studies of applying the QNN to both real data and confusing images synthesized by morphing. Tests on synthesized data examine QNN's fuzzy decision boundary with the intention to illustrate its mechanism and characteristics, while studies on real data prove its great potential as a handwritten numeral classifier and the special role it plays in multi-expert systems. An effective decision-fusion system is proposed and a high reliability of 99.10% has been achieved.
---
paper_title: OCR in a Hierarchical Feature Space

paper_content:

This paper describes hierarchical OCR, a character recognition methodology that achieves high speed and accuracy by using a multiresolution and hierarchical feature space. Features at different resolutions, from coarse to fine-grained, are implemented by means of a recursive classification scheme. Typically, recognizers have to balance the use of features at many resolutions (which yields a high accuracy), with the burden on computational resources in terms of storage space and processing time. We present in this paper, a method that adaptively determines the degree of resolution necessary in order to classify an input pattern. This leads to optimal use of computational resources. The hierarchical OCR dynamically adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences from patterns of other classes it is being compared against, and the processing time available. Furthermore, the finer resolution is accorded to only certain "zones" of the input pattern which are deemed important given the classes that are being discriminated. Experimental results support the methodology presented. When tested on standard NIST data sets, the hierarchical OCR proves to be 300 times faster than a traditional K-nearest-neighbor classification method, and 10 times taster than a neural network method. The comparison uses the same feature set for all methods. Recognition rate of about 96 percent is achieved by the hierarchical OCR. This is at par with the other two traditional methods.
---
paper_title: Combining classifiers based on minimization of a Bayes error rate

paper_content:

In order to raise a class discrimination power by combining multiple classifiers, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables should be minimized. Wang and Wong (1979) proposed a tree dependence approximation scheme of a high order probability distribution composed of those variables, based on minimizing the upper bound. In addition to that, this paper presents an extended approximation scheme dealing with higher order dependency. Multiple classifiers recognizing unconstrained handwritten numerals were combined by the proposed approximation scheme based on the minimization of the Bayes error rate, and the high recognition rates were obtained by them.
---
paper_title: Handwritten numerical recognition based on multiple algorithms

paper_content:

Abstract In this paper, the authors combine two algorithms for application to the recognition of unconstrained isolated handwritten numerals. The first algorithm employs a modified quadratic discriminant function utilizing direction sensitive spatial features of the numeral image. The second algorithm utilizes features derived from the profile of the character in a structural configuration to recognize the numerals. While both algorithms yield very low error rates, the authors combine the two algorithms in different ways to study the best polling strategy and realize very low error rates (0.2% or less) and rejection rates below 4%.
---
paper_title: Improving Performance in Neural Networks Using a Boosting Algorithm

paper_content:

A boosting algorithm converts a learning machine with error rate less than 50% to one with an arbitrarily low error rate. However, the algorithm discussed here depends on having a large supply of independent training samples. We show how to circumvent this problem and generate an ensemble of learning machines whose performance in optical character recognition problems is dramatically improved over that of a single network. We report the effect of boosting on four databases (all handwritten) consisting of 12,000 digits from segmented ZIP codes from the United State Postal Service (USPS) and the following from the National Institute of Standards and Testing (NIST): 220,000 digits, 45,000 upper case alphas, and 45,000 lower case alphas. We use two performance measures: the raw error rate (no rejects) and the reject rate required to achieve a 1% error rate on the patterns not rejected. Boosting improved performance in some cases by a factor of three.
---
paper_title: Automated forms-processing software and services

paper_content:

While document-image systems for the management of collections of documents, such as forms, offer significant productivity improvements, the entry of information from documents remains a labor-intensive and costly task for most organizations. In this paper, we describe a software system for the machine reading of forms data from their scanned images. We describe its major components: form recognition and “dropout,” intelligent character recognition (ICR), and contextual checking. Finally, we describe applications for which our automated forms reader has been successfully used.
---
paper_title: Off-line cursive script word recognition

paper_content:

Cursive script word recognition is the problem of transforming a word from the iconic form of cursive writing to its symbolic form. Several component processes of a recognition system for isolated offline cursive script words are described. A word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words. A unique feature representation is generated bottom-up from the image using statistical dependences between letters and features. Ratings for partially formed words are computed using a stack algorithm and a lexicon represented as a trie. Several novel techniques for low- and intermediate-level processing for cursive script are described, including heuristics for reference line finding, letter segmentation based on detecting local minima along the lower contour and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features, and finding shape-oriented events. Experiments demonstrating the performance of the system are also described. >
---
paper_title: Detection and correction of recognition errors in check reading

paper_content:

An important subtask in the automated reading of bank checks by an OCR program is the combination of the recognition results of the courtesy and the legal amount. In this paper we present some new techniques to solve this problem. First, a systematic approach for the translation of the legal amount into a digit string is described. The technique is based on syntax-directed translation. Once the result of the legal amount recognizer has been translated into the corresponding digit string, it can be easily compared to the result of the courtesy amount recognizer. Thus, inconsistencies between the legal and the courtesy amount can be detected and precisely located at the level of individual subwords in the legal amount and digits in the courtesy amount. Moreover, recognition errors can be potentially corrected. In experiments with real data from Swiss postal checks a significant improvement in the acceptance rate could be achieved without increasing the error rate of the overall system.
---
paper_title: An efficient algorithm for matching a lexicon with a segmentation graph

paper_content:

This paper presents an efficient algorithm for lexicon-driven handwritten word recognition. In this algorithm, a word image is represented by a segmentation graph, and the lexicon is represented by a trie. As opposed to the standard lexicon-driven matching approach, where dynamic programming is invoked independently for matching each entry in the lexicon against the segmentation graph, the proposed algorithm matches the trie with the segmentation graph. Computation is saved by the efficient representation of the lexicon using the trie data structure. The performance of the proposed approach is compared with the standard dynamic programming algorithm. The proposed approach saves about 48.4% (excluding the trie initialization cost) and 15% of computation time from the standard algorithm when a dynamic lexicon is used. Better performance can be expected in static lexicon cases where the trie needs to be constructed only once.
---
paper_title: An architecture for handwritten text recognition systems

paper_content:

This paper presents an end-to-end system for reading handwritten page images. Five functional modules included in the system are introduced in this paper: (i) pre-processing, which concerns introducing an image representation for easy manipulation of large page images and image handling procedures using the image representation; (ii) line separation, concerning text line detection and extracting images of lines of text from a page image; (iii) word segmentation, which concerns locating word gaps and isolating words from a line of text image obtained efficiently and in an intelligent manner; (iv) word recognition, concerning handwritten word recognition algorithms; and (v) linguistic post-pro- cessing, which concerns the use of linguistic constraints to intelligently parse and recognize text. Key ideas employed in each functional module, which have been developed for dealing with the diversity of handwriting in its various aspects with a goal of system reliability and robustness, are described in this paper. Preliminary experiments show promising results in terms of speed and accuracy.
---
paper_title: Design of a linguistic postprocessor using variable memory length Markov models

paper_content:

We describe a linguistic postprocessor for character recognizers. The central module of our system is a trainable variable memory length Markov model (VLMM) that predicts the next character given a variable length window of past characters. The overall system is composed of several finite state automata, including the main VLMM and a proper noun VLMM. The best model reported in the literature (Brown et al., 1992) achieves 1.75 bits per character on the Brown corpus. On that same corpus, our model, trained on 10 times less data, reaches 2.19 bits per character and is 200 times smaller (/spl sime/160,000 parameters). The model was designed for handwriting recognition applications but could also be used for other OCR problems and speech recognition.
---
paper_title: Lexicon-driven handwritten word recognition using optimal linear combinations of order statistics

paper_content:

In the standard segmentation-based approach to handwritten word recognition, individual character-class confidence scores are combined via averaging to estimate confidences in the hypothesized identities for a word. We describe a methodology for generating optimal linear combination of order statistics operators for combining character class confidence scores. Experimental results are provided on over 1000 word images.
---
paper_title: Techniques for automatically correcting words in text

paper_content:

Research aimed at correcting words in text has focused on three progressively more difficult problems:(1) nonword error detection; (2) isolated-word error correction; and (3) context-dependent work correction. In response to the first problem, efficient pattern-matching and n -gram analysis techniques have been developed for detecting strings that do not appear in a given word list. In response to the second problem, a variety of general and application-specific spelling correction techniques have been developed. Some of them were based on detailed studies of spelling error patterns. In response to the third problem, a few experiments using natural-language-processing tools or statistical-language models have been carried out. This article surveys documented findings on spelling error patterns, provides descriptions of various nonword detection and isolated-word error correction techniques, reviews the state of the art of context-dependent word correction techniques, and discusses research issues related to all three areas of automatic error correction in text.
---
paper_title: Off-line Arabic character recognition: the state of the art

paper_content:

Machine simulation of human reading has been the subject of intensive research for almost three decades. A large number of research papers and reports have already been published on Latin, Chinese and Japanese characters. However, little work has been conducted on the automatic recognition of Arabic characters because of the complexity of printed and handwritten text, and this problem is still an open research field. The main objective of this paper is to present the state of Arabic character recognition research throughout the last two decades.
---
paper_title: On-Line and Off-Line Handwriting Recognition : A Comprehensive Survey

paper_content:

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.
---

Title: An Overview Of Character Recognition
Section 1: INTRODUCTION
Description 1: Provide an introduction to character recognition (CR), its significance, the different types and stages, and the structure of the paper.

Section 2: HISTORY
Description 2: Discuss the historical developments and progress in character recognition, from early efforts to recent advancements.

Section 3: CHARACTER RECOGNITION (CR) SYSTEMS
Description 3: Classify character recognition systems based on data acquisition techniques and text type, and discuss the various methods and applications.

Section 4: METHODOLOGIES OF CR SYSTEMS
Description 4: Review the methodologies and techniques used in CR systems, covering pre-processing, segmentation, representation, training and recognition, and post-processing stages.

Section 5: DISCUSSION
Description 5: Summarize the current state of CR research, highlight existing challenges and limitations, and propose future research directions.