Improving Various Off-line Techniques used for Handwritten Character Recognition: a Review
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paper_title: Building a new generation of handwriting recognition systems

paper_content:

Abstract This paper gives an assessment of the current state of the art in handwriting recognition. It summarizes the lessons learned, the difficulties involved, and the challenges ahead. Based on a review of the recent achievements in off-line computer recognition of totally unconstrained handwritten characters, and extensive research, the authors attempt to identify new frontiers for research which may lead to further breakthroughs in this field. They will present some evidences and novel ideas on ways of stretching the limits of handwriting recognition systems aiming at outperforming human beings.
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paper_title: Adaptive, quadratic preprocessing of document images for binarization

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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.
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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.
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paper_title: Building a new generation of handwriting recognition systems

paper_content:

Abstract This paper gives an assessment of the current state of the art in handwriting recognition. It summarizes the lessons learned, the difficulties involved, and the challenges ahead. Based on a review of the recent achievements in off-line computer recognition of totally unconstrained handwritten characters, and extensive research, the authors attempt to identify new frontiers for research which may lead to further breakthroughs in this field. They will present some evidences and novel ideas on ways of stretching the limits of handwriting recognition systems aiming at outperforming human beings.
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paper_title: A survey of thresholding techniques

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Abstract In digital image processing, thresholding is a well-known technique for image segmentation. Because of its wide applicability to other areas of the digital image processing, quite a number of thresholding methods have been proposed over the years. In this paper, we present a survey of thresholding techniques and update the earlier survey work by Weszka (Comput. Vision Graphics & Image Process 7, 1978 , 259–265) and Fu and Mu (Pattern Recognit. 13, 1981 , 3–16). We attempt to evaluate the performance of some automatic global thresholding methods using the criterion functions such as uniformity and shape measures. The evaluation is based on some real world images.
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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.
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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.
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paper_title: Goal-Directed Evaluation of Binarization Methods

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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.
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paper_title: An adaptive logical method for binarization of degraded document images

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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.
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paper_title: Thinning methodologies−a comprehensive survey

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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. >
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paper_title: A robust skew detection algorithm for grayscale document image

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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.
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paper_title: Repulsive attractive network for baseline extraction on document images

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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.
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paper_title: Chaincode contour processing for handwritten word recognition

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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.
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paper_title: Handwritten English Character Recognition Using Neural Network

paper_content:

Neural Networks are recently being used in various kind of pattern recognition. Handwritings of different person are different; therefore it is very difficult to recognize the handwritten characters. Handwritten Character recognition is an area of pattern recognition that has become the subject of research during the last some decades. Neural network is playing an important role in handwritten character recognition. Many reports of character recognition in English have been published but still high recognition accuracy and minimum training time of handwritten English characters using neural network is an open problem. Therefore, it is a great important to develop an automatic handwritten character recognition system for English language [1]. In this paper, efforts have been made to develop automatic handwritten character recognition system for English language with high recognition accuracy and minimum training and classification time. Experimental result shows that the approach used in this paper for English character recognition is giving high recognition accuracy and minimum training time.
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paper_title: Direct extraction of topographic features for gray scale character recognition

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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. >
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paper_title: Feature Extraction Technique for Neural Network Based Pattern Recognition

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In this work, an attempt is made to extract minimum number of features to represent the pattern used as inputs for Feed Forward Back Propagation Neural Network (FFBPNN). The binary image of a pattern stored in the frame is partitioned into square regions. A feature from each region is computed by the density and co-ordinate distance of 1 s. pixels. The neural network is trained with the extracted features and Root Mean Square Error (RMSE) obtained in the training process is used as performance indicator to stop the FFBPNN learning. Tested the proposed feature extraction and classification algorithms on the handwritten numeral database and found very good classification recognition rate.
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paper_title: Diagonal Based Feature Extraction for Handwritten Alphabets Recognition System using Neural Network

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An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described in the paper. A new method, called, diagonal based feature extraction is introduced for extracting the features of the handwritten alphabets. Fifty data sets, each containing 26 alphabets written by various people, are used for training the neural network and 570 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.
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paper_title: Hand-Written Character Recognition Using Kohonen Network

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This paper presents a simple learning rule for recognition of mouse dragged character on our computer screen using artificial neural network. We use Kohonen self organization map for pattern classification which employs unsupervised learning algorithm. The results are quite encouraging in terms of percentage of characters being successfully recognized. One advantage of proposed scheme is that the system is quite tolerant to changing conditions and inputs. The system consistently learns. Moreover the recognition ratio is excellent in the proposed system.
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paper_title: Character Recognition Systems: A Guide for Students and Practitioners

paper_content:

Figures. List of Tables. Preface. Acknowledgments. Acronyms. 1. Introduction: Character Recognition, Evolution and Development. 1.1 Generation and Recognition of Characters. 1.2 History of OCR. 1.3 Development of New Techniques. 1.4 Recent Trends and Movements. 1.5 Organization of the Remaining Chapters. References. 2. Tools for Image Pre-Processing. 2.1 Generic Form Processing System. 2.2 A Stroke Model for Complex Background Elimination. 2.2.1 Global Gray Level Thresholding. 2.2.2 Local Gray Level Thresholding. 2.2.3 Local Feature Thresholding-Stroke Based Model. 2.2.4 Choosing the Most Efficient Character Extraction Method. 2.2.5 Cleaning up Form Items Using Stroke Based Model. 2.3 A Scale-Space Approach for Visual Data Extraction. 2.3.1 Image Regularization. 2.3.2 Data Extraction. 2.3.3 Concluding Remarks. 2.4 Data Pre-Processing. 2.4.1 Smoothing and Noise Removal. 2.4.2 Skew Detection and Correction. 2.4.3 Slant Correction. 2.4.4 Character Normalization. 2.4.5 Contour Tracing/Analysis. 2.4.6 Thinning. 2.5 Chapter Summary. References 72. 3. Feature Extraction, Selection and Creation. 3.1 Feature Extraction. 3.1.1 Moments. 3.1.2 Histogram. 3.1.3 Direction Features. 3.1.4 Image Registration. 3.1.5 Hough Transform. 3.1.6 Line-Based Representation. 3.1.7 Fourier Descriptors. 3.1.8 Shape Approximation. 3.1.9 Topological Features. 3.1.10 Linear Transforms. 3.1.11 Kernels. 3.2 Feature Selection for Pattern Classification. 3.2.1 Review of Feature Selection Methods. 3.3 Feature Creation for Pattern Classification. 3.3.1 Categories of Feature Creation. 3.3.2 Review of Feature Creation Methods. 3.3.3 Future Trends. 3.4 Chapter Summary. References. 4. Pattern Classification Methods. 4.1 Overview of Classification Methods. 4.2 Statistical Methods. 4.2.1 Bayes Decision Theory. 4.2.2 Parametric Methods. 4.2.3 Non-ParametricMethods. 4.3 Artificial Neural Networks. 4.3.1 Single-Layer Neural Network. 4.3.2 Multilayer Perceptron. 4.3.3 Radial Basis Function Network. 4.3.4 Polynomial Network. 4.3.5 Unsupervised Learning. 4.3.6 Learning Vector Quantization. 4.4 Support Vector Machines. 4.4.1 Maximal Margin Classifier. 4.4.2 Soft Margin and Kernels. 4.4.3 Implementation Issues. 4.5 Structural Pattern Recognition. 4.5.1 Attributed String Matching. 4.5.2 Attributed Graph Matching. 4.6 Combining Multiple Classifiers. 4.6.1 Problem Formulation. 4.6.2 Combining Discrete Outputs. 4.6.3 Combining Continuous Outputs. 4.6.4 Dynamic Classifier Selection. 4.6.5 Ensemble Generation. 4.7 A Concrete Example. 4.8 Chapter Summary. References. 5. Word and String Recognition. 5.1 Introduction. 5.2 Character Segmentation. 5.2.1 Overview of Dissection Techniques. 5.2.2 Segmentation of Handwritten Digits. 5.3 Classification-Based String Recognition. 5.3.1 String Classification Model. 5.3.2 Classifier Design for String Recognition. 5.3.3 Search Strategies. 5.3.4 Strategies for Large Vocabulary. 5.4 HMM-Based Recognition. 5.4.1 Introduction to HMMs. 5.4.2 Theory and Implementation. 5.4.3 Application of HMMs to Text Recognition. 5.4.4 Implementation Issues. 5.4.5 Techniques for Improving HMMs' Performance. 5.4.6 Summary to HMM-Based Recognition. 5.5 Holistic Methods For Handwritten Word Recognition. 5.5.1 Introduction to Holistic Methods. 5.5.2 Overview of Holistic Methods. 5.5.3 Summary to Holistic Methods. 5.6 Chapter Summary. References. 6. Case Studies. 6.1 Automatically Generating Pattern Recognizers with Evolutionary Computation. 6.1.1 Motivation. 6.1.2 Introduction. 6.1.3 Hunters and Prey. 6.1.4 Genetic Algorithm. 6.1.5 Experiments. 6.1.6 Analysis. 6.1.7 Future Directions. 6.2 Offline Handwritten Chinese Character Recognition. 6.2.1 Related Works. 6.2.2 System Overview. 6.2.3 Character Normalization. 6.2.4 Direction Feature Extraction. 6.2.5 Classification Methods. 6.2.6 Experiments. 6.2.7 Concluding Remarks. 6.3 Segmentation and Recognition of Handwritten Dates on Canadian Bank Cheques. 6.3.1 Introduction. 6.3.2 System Architecture. 6.3.3 Date Image Segmentation. 6.3.4 Date Image Recognition. 6.3.5 Experimental Results. 6.3.6 Concluding Remarks. References.
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paper_title: Handwritten Tamil Character Recognition and Conversion using Neural Network

paper_content:

Hand written Tamil Character recognition refers to the process of conversion of handwritten Tamil character into Unicode Tamil character. The scanned image is segmented into paragraphs using spatial space detection technique, paragraphs into lines using vertical histogram, lines into words using horizontal histogram, and words into character image glyphs using horizontal histogram. The extracted features considered for recognition are given to Support Vector Machine, Self Organizing Map, RCS, Fuzzy Neural Network and Radial Basis Network. Where the characters are classified using supervised learning algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts. This character recognition finds applications in document analysis where the handwritten document can be converted to editable printed document. This approach can be extended to recognition and reproduction of hand written documents in South Indian languages. In the training set, a recognition rate of 100% was achieved and in the test set the recognized speed for each character is 0.1sec and accuracy is 97%. Understandably, the training set produced much higher recognition rate than the test set. Structure analysis suggested that the proposed system of RCS with back propagation network is given higher recognition rate. Handwritten character recognition is a difficult problem due to the great variations of writing styles, different size and orientation angle of the characters. Among different branches of handwritten character recognition it is easier to recognize English alphabets and numerals than Tamil characters. Many researchers have also applied the excellent generalization capabilities offered by ANNs to the recognition of characters. Many studies have used fourier descriptors and Back Propagation Networks for classification tasks. Fourier descriptors were used in to recognize handwritten numerals. Neural Network approaches were used to classify tools. There have been only a few attempts in the past to address the recognition of printed or handwritten Tamil Characters. However, less attention had been given to Indian language recognition. Some efforts have been reported in the literature
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paper_title: Handwritten Tamil Character Recognition and Conversion using Neural Network

paper_content:

Hand written Tamil Character recognition refers to the process of conversion of handwritten Tamil character into Unicode Tamil character. The scanned image is segmented into paragraphs using spatial space detection technique, paragraphs into lines using vertical histogram, lines into words using horizontal histogram, and words into character image glyphs using horizontal histogram. The extracted features considered for recognition are given to Support Vector Machine, Self Organizing Map, RCS, Fuzzy Neural Network and Radial Basis Network. Where the characters are classified using supervised learning algorithm. These classes are mapped onto Unicode for recognition. Then the text is reconstructed using Unicode fonts. This character recognition finds applications in document analysis where the handwritten document can be converted to editable printed document. This approach can be extended to recognition and reproduction of hand written documents in South Indian languages. In the training set, a recognition rate of 100% was achieved and in the test set the recognized speed for each character is 0.1sec and accuracy is 97%. Understandably, the training set produced much higher recognition rate than the test set. Structure analysis suggested that the proposed system of RCS with back propagation network is given higher recognition rate. Handwritten character recognition is a difficult problem due to the great variations of writing styles, different size and orientation angle of the characters. Among different branches of handwritten character recognition it is easier to recognize English alphabets and numerals than Tamil characters. Many researchers have also applied the excellent generalization capabilities offered by ANNs to the recognition of characters. Many studies have used fourier descriptors and Back Propagation Networks for classification tasks. Fourier descriptors were used in to recognize handwritten numerals. Neural Network approaches were used to classify tools. There have been only a few attempts in the past to address the recognition of printed or handwritten Tamil Characters. However, less attention had been given to Indian language recognition. Some efforts have been reported in the literature
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paper_title: High Accuracy Myanmar Handwritten Character Recognition using Hybrid approach through MICR and Neural Network

paper_content:

This paper contributes an effective recognition approach for Myanmar Handwritten Characters. In this article, Hybrid approach use ICR and OCR recognition through MICR (Myanmar Intelligent Character Recognition) and backpropagation neural network. MICR is one kind of ICR. It composed of statistical/semantic information and final decision is made by voting system. In Hybrid approach, the features of statistical and semantic information of MICR have been used in back-propagation neural network as input nodes. So it needs a few input nodes to use. The back-propagation algorithm has been used to train the feed-forward neural network and adjustment of weights to require the desired output. The purpose of Hybrid approach to achieve the high accuracy rates and very fast recognition rate compare with other recognition systems. The experiments were carried out on 1000 words samples of different writer. Using Hybrid approach, over-all recognition accuracy of 95% was obtained.
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paper_title: Recognition of handwritten digits using template and model matching

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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.
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paper_title: Representation and Recognition of Handwritten Digits Using Deformable Templates

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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.
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paper_title: Cursive script recognition by elastic matching

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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.
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Title: Improving Various Off-line Techniques used for Handwritten Character Recognition: a Review

Section 1: INTRODUCTION
Description 1: This section provides an overview of character recognition, distinguishing off-line recognition and its applications, and highlights the challenges and advantages of off-line recognizers.

Section 2: ARCHITECTURE OF A GENERAL CHARACTER RECOGNITION SYSTEM
Description 2: This section details the key stages of a character recognition system, including image acquisition, preprocessing, and noise removal.

Section 3: Image Acquisition
Description 3: This subsection explains the process of capturing input images through devices such as cameras or scanners.

Section 4: Image Preprocessing
Description 4: This subsection discusses the conversion of input images into binary form and elaborates on the initial preprocessing techniques applied.

Section 5: Noise removal
Description 5: This subsection covers various techniques employed to clean and enhance the image by removing noise, ensuring better accuracy and performance.

Section 6: Filtering
Description 6: This subsection describes the filtering techniques used to remove noise and improve image quality through spatial and frequency domain filters.

Section 7: Morphological Operations
Description 7: This subsection outlines the morphological operations used to refine document images by connecting strokes, smoothing contours, and extracting boundaries.

Section 8: Thresholding
Description 8: This subsection explains techniques to differentiate the foreground (ink) from the background (paper) using global and local threshold values.

Section 9: Skeletonization
Description 9: This subsection details the process of reducing the binary image regions to lines approximating the skeletons of the regions.

Section 10: Normalization
Description 10: This subsection discusses size normalization techniques to standardize image sizes and correct skew and slant in handwritten characters.

Section 11: Segmentation
Description 11: This section describes the methods to segment the document into lines, words, and characters, crucial for accurate recognition.

Section 12: Explicit Segmentation
Description 12: This subsection explains the identification of segments based on character-like properties through a dissection process.

Section 13: Implicit Segmentation
Description 13: This subsection covers segmentation based on recognition, using recognition confidence and predefined class matches.

Section 14: Feature Extraction
Description 14: This subsection discusses the extraction of relevant features from segmented images, crucial for character recognition.

Section 15: Classification
Description 15: This section outlines the process of assigning class labels to the feature vectors obtained, using various supervised and unsupervised methods.

Section 16: Methodology
Description 16: This section describes the methodologies of pattern recognition employed in character recognition systems.

Section 17: Template Matching
Description 17: This subsection discusses techniques of character recognition based on matching stored prototypes against input images.

Section 18: Direct Matching
Description 18: This subsection explains the straightforward comparison of input characters to stored patterns based on similarity measures.

Section 19: Deformable Templates and Elastic Matching
Description 19: This subsection details methods using image deformation to match unknown images with known images.

Section 20: Statistical Techniques
Description 20: This subsection covers techniques using statistical decision theory to classify characters based on feature sets and probabilities.

Section 21: Non-parametric Recognition
Description 21: This subsection explains the nearest neighbor method, which classifies patterns based on minimum distance from cluster centers.

Section 22: Parametric Recognition
Description 22: This subsection details classification methods using parametric models based on a priori information about character classes.

Section 23: Structural Techniques
Description 23: This section outlines methods representing characters as combinations of structural primitives.

Section 24: Grammatical Methods
Description 24: This subsection describes the use of grammatical rules combining statistical and topological features for character recognition.

Section 25: Neural Networks (NN)
Description 25: This subsection covers the application of artificial neural networks in character recognition, highlighting various architectures and training approaches.

Section 26: CONCLUSION
Description 26: This section summarizes the review of different techniques used in handwritten character recognition, discussing their effectiveness and future possibilities.

Section 27: FUTURE SCOPE
Description 27: This section suggests areas for further research to improve recognition rates by using additional features, advanced classifiers, and addressing challenges in recognizing complex character strings.