Offline cursive script word recognition – a survey
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paper_title: Optical character recognition ― A survey

paper_content:

In order to highlight the interesting problems and actual results on the state of the art in optical character recognition (OCR), this paper describes and compares preprocessing, feature extraction and postprocessing techniques for commercial reading machines. Problems related to handwritten and printed character recognition are pointed out, and the functions and operations of the major components of an OCR system are described. Historical background on the development of character recognition is briefly given and the working of an optical scanner is explained. The specifications of several recognition systems that are commercially available are reported and compared.
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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.
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paper_title: Feature extraction methods for character recognition-A survey

paper_content:

This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters) or gray-level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application.
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paper_title: Machine recognition of handwritten words: A project report

paper_content:

Abstract A project beginning with publication of the author's book Recognition in 1965 has resulted in a cursive script recognition program which has correctly identified 79 per cent of a test sample of 84 words. This program makes no use of information about sequence of stroke inscription, and segments the cursive line as part of the recognition procedure. It thus compares favorably in performance level with previously reported programs appropriately “normalized”, while not requiring input pertaining to stroke sequence and stroke segmentation that is essential to these other programs.
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paper_title: A multi-classifier combination strategy for the recognition of handwritten cursive words

paper_content:

A recognition scheme for reading handwritten cursive words using three word recognition techniques is described. The focus is on the implementation used to combine the three techniques based on a comparative study of different strategies. The first holistic recognition technique derives a global encoding of the word. The other techniques both rely on the segmentation of the word into letters, but differ in the character classifier they use. The former runs a statistical linear classifier, and the latter runs a neural network with a different representation of the input data. The testing, comparison, and combination studies have been performed on word images from mail provided by the USPS. The top choice recognition rates achieved so far correspond to 88%, 76%, 65% with respect to lexicon sizes of 10, 100, and 1000 words. >
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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. >
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paper_title: Character segmentation in handwritten words - An overview

paper_content:

Abstract This paper is the second part of a review series on the character segmentation techniques. In this paper, we present an overview on the most important techniques used in segmenting characters from handwritten words. It is well-recognized that it is difficult to segment individual characters from handwritten words without the support from recogniton and context analysis. One common characteristic of all the existing handwritten word recogniton algorithms is that the character segmentation process is closely coupled with the recognition process. This review consists of three major portions, handprinted word segmentation, handwritten numeral segmentation and cursive word segmentation. Every algorithm discussed in the paper is accompanied with a flow chart to give a clear grasp of the algorithm. One section summarizes the terms and measurements commonly used in handwritten character segmentation. The bibliography contains a comprehensive list of work in handwritten character segmentation and recognition.
---
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.
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paper_title: A Method for the Correction of Garbled Words Based on the Levenshtein Metric

paper_content:

In this paper we propose a new method for correcting garbled words based on Levenshtein distance and weighted Levenshtein distance. We can correct not only substitution errors, but also insertion errors and deletion errors by this method. According to the results of simulation on nearly 1000 high occurrence English words, higher error correcting rates can be achieved by this method than any other method tried to date. Hardware realization of the method is possible, though it is rather complicated.
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paper_title: Character segmentation in handwritten words - An overview

paper_content:

Abstract This paper is the second part of a review series on the character segmentation techniques. In this paper, we present an overview on the most important techniques used in segmenting characters from handwritten words. It is well-recognized that it is difficult to segment individual characters from handwritten words without the support from recogniton and context analysis. One common characteristic of all the existing handwritten word recogniton algorithms is that the character segmentation process is closely coupled with the recognition process. This review consists of three major portions, handprinted word segmentation, handwritten numeral segmentation and cursive word segmentation. Every algorithm discussed in the paper is accompanied with a flow chart to give a clear grasp of the algorithm. One section summarizes the terms and measurements commonly used in handwritten character segmentation. The bibliography contains a comprehensive list of work in handwritten character segmentation and recognition.
---
paper_title: A multi-classifier combination strategy for the recognition of handwritten cursive words

paper_content:

A recognition scheme for reading handwritten cursive words using three word recognition techniques is described. The focus is on the implementation used to combine the three techniques based on a comparative study of different strategies. The first holistic recognition technique derives a global encoding of the word. The other techniques both rely on the segmentation of the word into letters, but differ in the character classifier they use. The former runs a statistical linear classifier, and the latter runs a neural network with a different representation of the input data. The testing, comparison, and combination studies have been performed on word images from mail provided by the USPS. The top choice recognition rates achieved so far correspond to 88%, 76%, 65% with respect to lexicon sizes of 10, 100, and 1000 words. >
---
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. >
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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. >
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paper_title: A Multi-Level Perception Approach to Reading Cursive Script

paper_content:

Abstract Reading cursive script is the problem of transforming language from the iconic form of cursive human handwriting to one of ordinal representation. The problem involves elements of visual perception at one level of processing and those of language perception and understanding at a higher level. The problem is approached as one of multi-level perception in which a cursive script word image is transformed through a representational hierarchy. The levels are based on descriptions that use points, contours, features, letters, and words. Global control is hierarchical until an intermediate level after which it is heterarchical. A feature representation is generated bottom-up from the image using statistical dependencies between letters and features. Ratings for partially formed words are computed using a stack and a lexicon represented as a trie. Several heuristics for low- and intermediate-level processing for cursive script are introduced, including: reference-line finding using projection profile analysis, letter segmentation based on local lower contour minima and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features (e.g., middle loop, upper-zone stroke), and finding shape-oriented events. Two modes of learning are defined: initial training with user feedback and unsupervised adaptation to the writer. Experiments demonstrating the promise of the approach are described.
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paper_title: Experiments with handwriting recognition using holographic representation of line images

paper_content:

Abstract This paper describes a new representation technique for handwritten words or other types of line images. The goal is to find a description convenient both for goal-driven and data-driven recognition processes. A representation is based on mapping of line segments approximating the word into a special feature space called holograph. The decision-making process is organized at word- and phrase-analysis level. Experiments with recognition of handwritten words from banking cheques are described.
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paper_title: Global word shape processing in off-line recognition of handwriting

paper_content:

Off-line recognition of handwriting may be achieved using simplified profiles of word shapes. These profiles consist of approximations of the word's upper and lower contour. Training and recognition are based on n-gram extraction and identification. The lexicons used extend to 16,000 words.
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paper_title: Holistic lexicon reduction for handwritten word recognition

paper_content:

The holistic paradigm in HWR has been applied to recognition scenarios involving small, static lexicons, such as the check amount recognition task. In this paper, we explore the possibility of using holistic information for lexicon reduction when the lexicons are large or dynamic, and training, in the traditional sense of learning decision surfaces from training samples of each class, is not viable. Two experimental lexicon reduction methods are described. The first uses perceptual features such as ascenders, descenders and length and achieves consistent reduction performance with cursive, discrete and mixed writing styles. A heuristic feature-synthesis algorithm is used to 'predict' holistic features of lexicon entries, which are matched against image features using a constrained bipartite graph matching scheme. With essentially unconstrained handwritten words, this system achieves reduction of 50% with less than 2% error. More effective reduction can be achieved if the problem can be constrained by making assumptions about the nature of input. The second classifier described operates on pure cursive script and achieves effective reduction of large lexicons of the order of 20,000 entries. Downstrokes are extracted from the contour representation of cursive words by grouping local extrema using a small set of heuristic rules. The relative heights of downstrokes are captured in a string descriptor that is syntactically matched with lexicon entries using a set of production rules. In initial tests, the system achieved high reduction (99%) at the expense of accuracy (75%).
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paper_title: Holistic handwritten word recognition using temporal features derived from off-line images

paper_content:

This paper describes an algorithm for holistic recognition of off-line cursive words using temporal stroke information derived from off-line script. Temporal information is extracted by traversing the strokes without explicit segmentation of the word into constituent characters. The word image is then mapped on to a feature vector matrix of uptrends and downtrends of strokes. This feature vector matrix is compared to prestored feature vector of lexicon entries and ranked accordingly. On a test set of images, the temporal feature extraction rate is 80%. Given the correct set of temporal features, the recognition rate of the holistic classifier is 81% on small lexicons.
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paper_title: Pruning large lexicons using generalized word shape descriptors

paper_content:

We present a technique for pruning of large lexicons for recognition of cursive script words. The technique involves extraction and representation of downward pen-strokes from the cursive word (off-line or online) to obtain a generalized descriptor which provides a coarse characterization of word shape. The descriptor is matched with ideal descriptors of lexicon entries organized as a trie. When used with a static lexicon of 21,000 words, the accuracy of reduction to 1000 words exceeds 95%.
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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.
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paper_title: Recognition of handwritten words using stochastic models

paper_content:

The paper deals with the global recognition of a small lexicon of words, based on a pseudo segmentation stage introducing anchor points. We avoid the difficult problem of segmentating the word into letters and the complexity involved by such models to build possible letter graphs. We use two structural representations of the word, strokes and graphemes, each of them being analyzed using a Markov model. These simple models are individually optimized by a rigorous choice of the order for fitting the structural properties of the observed data using Akaike information criteria. The conditional probability to have a word model, given the observation sequence, is computed by taking into account the length of the sequence. Results of the study are presented on French cheque images.
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paper_title: An optimised minimal edit distance for hand-written word recognition

paper_content:

In this paper, we discuss a method for optimising the parameters of the substitution costs of the minimal edit distance. A discriminative error criterion is proposed for the optimisation. The modelling of the substitution costs, which is application-dependant, is discussed. An important observation is that many models for the substitution costs are possible. With the appropriate substitution costs it is possible, for example, to compute the minimal edit distance between a string and a sequence of real vectors. In a basic experiment the proposed method has been successfully tested on a string-to-string matching problem. The strings were derived by explicit character segmentation and character recognition of hand-written Dutch city names taken from live mail. Having verified that the proposed optimisation works on a simple problem, we can now test the optimised minimal edit distance on more challenging word recognition problems, without explicit segmentation and character recognition.
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paper_title: Holistic lexicon reduction for handwritten word recognition

paper_content:

The holistic paradigm in HWR has been applied to recognition scenarios involving small, static lexicons, such as the check amount recognition task. In this paper, we explore the possibility of using holistic information for lexicon reduction when the lexicons are large or dynamic, and training, in the traditional sense of learning decision surfaces from training samples of each class, is not viable. Two experimental lexicon reduction methods are described. The first uses perceptual features such as ascenders, descenders and length and achieves consistent reduction performance with cursive, discrete and mixed writing styles. A heuristic feature-synthesis algorithm is used to 'predict' holistic features of lexicon entries, which are matched against image features using a constrained bipartite graph matching scheme. With essentially unconstrained handwritten words, this system achieves reduction of 50% with less than 2% error. More effective reduction can be achieved if the problem can be constrained by making assumptions about the nature of input. The second classifier described operates on pure cursive script and achieves effective reduction of large lexicons of the order of 20,000 entries. Downstrokes are extracted from the contour representation of cursive words by grouping local extrema using a small set of heuristic rules. The relative heights of downstrokes are captured in a string descriptor that is syntactically matched with lexicon entries using a set of production rules. In initial tests, the system achieved high reduction (99%) at the expense of accuracy (75%).
---
paper_title: A multi-classifier combination strategy for the recognition of handwritten cursive words

paper_content:

A recognition scheme for reading handwritten cursive words using three word recognition techniques is described. The focus is on the implementation used to combine the three techniques based on a comparative study of different strategies. The first holistic recognition technique derives a global encoding of the word. The other techniques both rely on the segmentation of the word into letters, but differ in the character classifier they use. The former runs a statistical linear classifier, and the latter runs a neural network with a different representation of the input data. The testing, comparison, and combination studies have been performed on word images from mail provided by the USPS. The top choice recognition rates achieved so far correspond to 88%, 76%, 65% with respect to lexicon sizes of 10, 100, and 1000 words. >
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paper_title: Cursive script recognition applied to the processing of bank cheques

paper_content:

A method for recognizing unconstrained handwritten words belonging to a small static lexicon is proposed. Our computational theory is based on a psychological model of the reading process of a fast reader. The method we propose is global in its nature and avoid the difficult segmentation stage of common word recognition techniques. Our computational theory has been applied to the processing of handwritten bank cheques, whose problem domain is that of unconstrained handwriting, unlimited writers in a small static lexicon. Current results seem comparable to those published in the literature and support our computational theory.
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paper_title: Recognition of handwritten word images by statistical methods

paper_content:

A demand type heart pacer which provides electronically generated stimulating pulses to the heart at a first frequency in the continued absence of natural heartbeats, but which inhibits output pulses once the natural heart rate exceeds the first frequency, and allows the heart to beat naturally at any rate above a second, lower frequency before again providing stimulating pulses, thereby creating a different standby frequency. The pacer also reverts to a third frequency in a non-demand type operation in the presence of an interfering electrical noise pattern.
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paper_title: An off-line cursive handwriting recognition system

paper_content:

Describes a complete system for the recognition of off-line handwriting. Preprocessing techniques are described, including segmentation and normalization of word images to give invariance to scale, slant, slope and stroke thickness. Representation of the image is discussed and the skeleton and stroke features used are described. A recurrent neural network is used to estimate probabilities for the characters represented in the skeleton. The operation of the hidden Markov model that calculates the best word in the lexicon is also described. Issues of vocabulary choice, rejection, and out-of-vocabulary word recognition are discussed.
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paper_title: Modeling and recognition of cursive words with hidden Markov models

paper_content:

In this paper, a new method for modeling and recognizing cursive words with hidden Markov models (HMM) is presented. In the proposed method, a sequence of thin fixed-width vertical frames are extracted from the image, capturing the local features of the handwriting. By quantizing the feature vectors of each frame, the input word image is represented as a Markov chain of discrete symbols. A handwritten word is regarded as a sequence of characters and optional ligatures. Hence, the ligatures are also explicitly modeled. With this view, an interconnection network of character and ligature HMMs is constructed to model words of indefinite length. This model can ideally describe any form of handwritten words, including discretely spaced words, pure cursive words and unconstrained words of mixed styles. Experiments have been conducted with a standard database to evaluate the performance of the overall scheme. The performance of various search strategies based on the forward and backward score has been compared. Experiments on the use of a preclassifier based on global features show that this approach may be useful for even large-vocabulary recognition tasks.
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paper_title: The viterbi algorithm

paper_content:

The Viterbi algorithm (VA) is a recursive optimal solution to the problem of estimating the state sequence of a discrete-time finite-state Markov process observed in memoryless noise. Many problems in areas such as digital communications can be cast in this form. This paper gives a tutorial exposition of the algorithm and of how it is implemented and analyzed. Applications to date are reviewed. Increasing use of the algorithm in a widening variety of areas is foreseen.
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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. >
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paper_title: Handwritten word recognition using continuous density variable duration hidden Markov model

paper_content:

A complete system for the recognition of unconstrained handwritten words using a continuous density variable duration hidden Markov model (CDVDHMM) is described. First, a novel segmentation algorithm based on mathematical morphology is developed to translate the 2-D image into a 1-D sequence of subcharacter symbols. This sequence of symbols is modeled by the CDVDHMM. Thirty-five features are selected to represent the character symbols in the feature space. Generally, there are two information sources associated with written text: the shape information and the linguistic knowledge. While the shape information of each character symbol is modeled as a mixture Gaussian distribution, the linguistic knowledge, i.e., constraint, is modeled as a Markov chain. The variable duration state is used to take care of the segmentation ambiguity among the consecutive characters. Detailed experiments were carried out using handwritten city names, and successful recognition results are reported. >
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paper_title: Off-line handwritten word recognition (HWR) using a single contextual hidden Markov model

paper_content:

A complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model (HMM) is proposed. The scheme includes a morphology- and heuristics-based segmentation algorithm and a modified Viterbi algorithm that searches the (l+1)st globally best path based on the previous l best paths. The results of detailed experiments for which the overall recognition rate is up to 89.4% are reported. >
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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.
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paper_title: Conjoined location and recognition of street names within a postal address delivery line

paper_content:

This paper describes a global model designed to jointly detect and recognize a street name within a delivery line of an handwritten address block image. The model used is based on Hidden Markov Models (HMM). The lines are firstly preprocessed, then segmented and characterized by two types of features. We create a HMM for each street name by simply concatenating the corresponding letter models, elementary HMM learned on a large city name database. A street name may often be surrounded by other information on the left and on the right within the delivery line. These phenomena are roughly modelled by trigrams. The global model is then simply obtained by the concatenation of trigram models with the HMMs corresponding to street names.
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paper_title: HMM word recognition engine

paper_content:

We describe a hidden Markov model (HMM) based word recognition engine being developed to be integrated with the CENPARMI bank cheque processing system. The various modules are described in detail, and preliminary results are compared with our previous global feature recognition scheme. The engine is tested on words from a database of over 4,500 cheques of 1,400 writers.
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paper_title: Strategies for cursive script recognition using hidden Markov models

paper_content:

We describe several approaches for the application of hidden Markov models to the recognition of handwritten words. In all approaches the words are described by strings of symbols. The descriptions differ only with respect to the size of the vocabulary to be recognized. We can define two distinct cases: in the first, the vocabulary is small and constant; in the second, the vocabulary is limited, but dynamic in the sense that it is a varying subset of an open one. We also describe an application of hidden Markov models to the representation of contextual knowledge and propose some strategies for rejecting unreliable word interpretations, in particular, when the word corresponding to the image does not necessarily belong to the lexicon.
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paper_title: Recognition of cursive script amounts on postal cheques

paper_content:

An impact printer in which characters moving on an endless chain are struck on-the-fly by print hammers to print the characters on paper. The print hammers are springs supported in flexed conditions by a permanent magnet, and the springs are released by applying an electrical signal to a coil which creates a magnetic field balancing the permanent magnet. Special supports provide different effective lengths for the spring in the cocked and printing positions.
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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.
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paper_title: Off-line handwritten word recognition using HMM with adaptive length Viterbi algorithm

paper_content:

In this paper, we have developed a handwritten word recognition scheme based on a single contextual hidden Markov model (HMM) incorporated with an adaptive length Viterbi algorithm. This work attempts to extend our earlier HMM scheme for naturally segmented word recognition to cursive and nonsegmented word recognition. The algorithm pre-segments the script into characters and/or fractions of characters, dynamically selects the optimal segmentation points, determines the word length, and recognizes the word according to the maximum path probability. The HMM is on top of, but independent of, script segmentation and character recognition techniques, and therefore can be further improved by incorporating more refined segmentation and character recognition procedure. The experiments have shown promising results.
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paper_title: Strategies for handwritten words recognition using hidden Markov models

paper_content:

Several approaches for the application of hidden Markov models to the recognition of handwritten words are described. All approaches share the same description of words through strings of symbols. They differ with respect to the size of the vocabulary which has to be recognized. The authors distinguish between two cases: where the vocabulary is small and constant, and where the vocabulary is limited but dynamic in the sense that it is a varying subset of an open one. The authors also describe an application of hidden Markov models to the representation of contextual knowledge and propose some strategies to reject unreliable word interpretations, in particular when the word corresponding to the image is not guaranteed to belong to the lexicon. >
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paper_title: Handwritten word recognition using HMM with adaptive length Viterbi algorithm

paper_content:

The authors have developed a handwritten word recognition scheme based on a single contextual, discrete symbol probability hidden Markov model (HMM) incorporated with an adaptive length Viterbi algorithm. This work attempts to extend the earlier HMM scheme for naturally segmented word recognition to cursive and nonsegmented word recognition. The algorithm presegments the script into characters and/or fractions of characters, dynamically selects the correct segmentation points, determines the word length, and recognizes the word according to the maximum path probability. The HMM is on top of, but independent of, script segmentation and character recognition techniques, and therefore leaves room for further improvement. The experiments have shown promising results and directions for further improvement. >
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paper_title: Writer adaptation for handwritten word recognition using hidden Markov models

paper_content:

This paper describes a method for improving handwritten word recognition by implicitly recognizing the writing style. Writing style is taken here to cover several types of distinctions between word shapes: cursive script vs. handprinted words, run-on vs. discrete words, differences in skew angle values, stability of lower and upper extensions of letters, presence or absence of loops in naturally looped letters. This method is applied in the general framework of hidden Markov models (HMM). The proposed method consists in the use of a set of models rather than of a unique model for each word. Each model in the set is associated to a certain class of writers, As a consequence, writing style is automatically and implicitly detected during recognition. But the main contribution of the method is a better estimation of the recognition score, i.e. the probability of generating the word with the HMM.
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paper_title: Recognition of handwritten words in a limited dynamic vocabulary

paper_content:

Tomatoes or the like are removed from a mass of vines and tomatoes by advancing and shaking the mass to detach some of the fruit, dropping the mass while turning it over, advancing the inverted mass along a second path in a direction opposite that of the first path while further shaking the inverted mass for detaching additional fruit. These steps are carried out on endless shaking conveyors that are oscillated by rotating kicker bar assemblies. An undershaker conveyor is disposed beneath the lower reach of each of the shaker conveyors and runs in the direction of that reach. The lower shaker conveyor extends past the delivery end of the upper shaking conveyor for facilitating the turning over action between the shakers. Sickle bars are provided at the delivery end of each shaker conveyor for cutting up vines and preventing them from being picked up by their associated undershaker conveyors.
---
paper_title: The viterbi algorithm

paper_content:

The Viterbi algorithm (VA) is a recursive optimal solution to the problem of estimating the state sequence of a discrete-time finite-state Markov process observed in memoryless noise. Many problems in areas such as digital communications can be cast in this form. This paper gives a tutorial exposition of the algorithm and of how it is implemented and analyzed. Applications to date are reviewed. Increasing use of the algorithm in a widening variety of areas is foreseen.
---
paper_title: Strategies for cursive script recognition using hidden Markov models

paper_content:

We describe several approaches for the application of hidden Markov models to the recognition of handwritten words. In all approaches the words are described by strings of symbols. The descriptions differ only with respect to the size of the vocabulary to be recognized. We can define two distinct cases: in the first, the vocabulary is small and constant; in the second, the vocabulary is limited, but dynamic in the sense that it is a varying subset of an open one. We also describe an application of hidden Markov models to the representation of contextual knowledge and propose some strategies for rejecting unreliable word interpretations, in particular, when the word corresponding to the image does not necessarily belong to the lexicon.
---
paper_title: Recognition of cursive script amounts on postal cheques

paper_content:

An impact printer in which characters moving on an endless chain are struck on-the-fly by print hammers to print the characters on paper. The print hammers are springs supported in flexed conditions by a permanent magnet, and the springs are released by applying an electrical signal to a coil which creates a magnetic field balancing the permanent magnet. Special supports provide different effective lengths for the spring in the cocked and printing positions.
---
paper_title: Strategies for handwritten words recognition using hidden Markov models

paper_content:

Several approaches for the application of hidden Markov models to the recognition of handwritten words are described. All approaches share the same description of words through strings of symbols. They differ with respect to the size of the vocabulary which has to be recognized. The authors distinguish between two cases: where the vocabulary is small and constant, and where the vocabulary is limited but dynamic in the sense that it is a varying subset of an open one. The authors also describe an application of hidden Markov models to the representation of contextual knowledge and propose some strategies to reject unreliable word interpretations, in particular when the word corresponding to the image is not guaranteed to belong to the lexicon. >
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paper_title: Handwritten word recognition for real-time applications

paper_content:

A fast method of handwritten word recognition suitable for real time applications is presented in this paper. Preprocessing, segmentation and feature extraction are implemented using a chain code representation of the word contour. Dynamic matching between characters of a lexicon entry and segment(s) of the input word image is used to rank the lexicon entries in order of best match. Variable duration for each character is defined and used during the matching. Experimental results prove that our approach using the variable duration outperforms the method using fixed duration in terms of both accuracy and speed. Speed of the entire recognition process is about 200 msec on a single SPARC-10 platform and the recognition accuracy is 96.8 percent are achieved for lexicon size of 10, on a database of postal words captured at 212 dpi.
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paper_title: Recognition of handwritten phrases as applied to street name images

paper_content:

A method for recognition of street name phrases collected from mail pieces is presented in this paper. Some of the challenges posed by the problem are: (i) patron errors, (ii) non-standardized way of abbreviating names, and (iii) variable number of words in a street name image. A neural network has been designed to segment words in a phrase, a street name in this case, using distances between components and style of writing. The network learns the type of spacing (including size) that one should expect between different pairs of characters in handwritten test. Experiments show perfect word segmentation performance at about 85% of cases. Unlike conventional methods, where lexicon entries are expanded to take care of all variations of prefixes and sizes, substring matching is attempted only between the main body of a lexicon entry and the word segments of an image. Efforts to reduce computational complexity are successfully made by the sharing of character segmentation results between the segmentation and recognition phases. 83% phrase recognition accuracy is achieved on a test set.
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paper_title: Segmentation-based handwritten word recognition

paper_content:

A finger pad for utilization in connection with angled dental instruments such as pluggers, margin trimmers, enamel hatchets, etc. The finger pad includes a piece of plastic or similar material mounted on the angled portion of the instrument and provided with pressure points contoured into the pad for reception of the tip of the operator's finger. The contoured portions are positioned so that a direct pressure is properly directed toward the tip of the tool thereby eliminating inaccuracies. The pad can either be removably placed on the angled portion of the tool, permanently secured to the end of the tool handle or made integral therewith.
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paper_title: Handwritten phrase recognition as applied to street name images

paper_content:

A phrase recognition method for recognition of street name images is presented in this paper. Some of the challenges posed by the problem are: (i) patron errors, (ii) non-standardized way of abbreviating names, and (iii) variable number of words in a street name image. A neural network has been designed to segment words in a phrase, using distance between components and style of writing. Experiments show perfect word segmentation performance of 85%. Substring matching is attempted only between the main body of a lexicon entry and the word segments of an image. Efforts to reduce computational complexity are successfully made by the sharing of character segmentation results between the segmentation and recognition phases. 83% phrase recognition accuracy was achieved on a test set.
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paper_title: Handwritten word recognition with character and inter-character neural networks

paper_content:

An off-line handwritten word recognition system is described. Images of handwritten words are matched to lexicons of candidate strings. A word image is segmented into primitives. The best match between sequences of unions of primitives and a lexicon string is found using dynamic programming. Neural networks assign match scores between characters and segments. Two particularly unique features are that neural networks assign confidence that pairs of segments are compatible with character confidence assignments and that this confidence is integrated into the dynamic programming. Experimental results are provided on data from the U.S. Postal Service.
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paper_title: Handwritten word recognition using lexicon free and lexicon directed word recognition algorithms

paper_content:

The paper discusses the relative merits and complexities of two word recognition algorithms: lexicon directed and lexicon free techniques. This algorithm operates on a pre-segmented word image and yields the optimum concatenation of the image segments for each word in the lexicon. However, the computational complexity of this algorithm is quite high, as the optimum concatenation is required for every word in the lexicon. In the lexicon free word matching process, the character likelihood for all the letters are calculated and the maximum likelihood value and the associated letter are determined. In this approach an optimum string results from the concatenation process. The word matching process is applied only once for an input word image. Comparative results with regard to accuracy, speed and size of lexicon are presented.
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paper_title: Cursive script segmentation and recognition by dynamic programming

paper_content:

Traditionally, the problem of cursive script recognition has been handled in two fundamental ways: one based on the global approach and the other on the segmentation approach. In this paper, we present an inexact segmentation approach to segment the word into letters or even strokes. Our algorithm searches for high curvature points along the lower contour of the image profile. These points are then treated as segment points and marked as potential letter boundaries. Using the histogram profile, an initial estimation of the word length is made. Yet, this estimated word length may be adjusted later. In the process, dynamic programming is used as a general optimization technique to produce a list of possible candidates of the same word length. If the score of these candidates cannot meet some recognition criteria, the word length is re-estimated and another set of candidates are generated. Eventually, an entropy- based measure is provided for comparison among candidates of different word length. In our system, a contextual postprocessor can easily be added to further improve the recognition rate. But in this instance, we have an additional advantage in that a large number of candidates of variable word length are available for selection and even some words with unrecognized letters may also be taken into consideration. In this paper, some experiments are also carried out to evaluate the performance of our novel algorithm.
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paper_title: Improvements of a lexicon directed algorithm for recognition of unconstrained handwritten words

paper_content:

Discusses improvements made to a lexicon directed algorithm for recognition of unconstrained handwritten words (cursive, discrete, or mixed) such as those encountered in mail pieces. The procedure consists of binarization, pre-segmentation, intermediate feature extraction, segmentation recognition, and post-processing. The segmentation recognition and the post-processing are repeated for all lexicon words while the binarization to the intermediate feature extraction are applied once for an input word. The result of performance evaluation using large handwritten address block database is described, and algorithm improvements are described and discussed, in order to achieve higher recognition accuracy and speed. As a result the performance for lexicons of size 10, 100, and 1000 are improved to 98.01%, 95.46%, and 91.49% respectively. The processing speed for each lexicon is improved to 2.0, 2.5, and 3.5 sec/word on a SUN SPARC station 2. >
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paper_title: General word recognition using approximate segment-string matching

paper_content:

Focuses on the problem of isolated off-line general word recognition using an approximate stroke-segment/string matching algorithm. Several recently proposed word recognition algorithms use the strategy of directly matching the stroke segments (with OCR estimates) to the sequence of characters in each lexicon word. This idea works very well under ideal conditions; however, many applications require the recognition of text in the presence of document noise, poor handwriting and lexicon errors. These factors require careful design of the matching strategy such that a moderate amount of any form of degradation does not cause a recognition failure. A segment-to-string matching algorithm is proposed which robustly recovers from moderate levels of noise and system errors. This algorithm is developed in the context of a complete word recognition system and serves as its final post-processing module.
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paper_title: The viterbi algorithm

paper_content:

The Viterbi algorithm (VA) is a recursive optimal solution to the problem of estimating the state sequence of a discrete-time finite-state Markov process observed in memoryless noise. Many problems in areas such as digital communications can be cast in this form. This paper gives a tutorial exposition of the algorithm and of how it is implemented and analyzed. Applications to date are reviewed. Increasing use of the algorithm in a widening variety of areas is foreseen.
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paper_title: Recognition of General Handwritten Words Using a Hypothesis Generation and Reduction Methodology

paper_content:

Safety mechanism for an individually fulcrummed rocking lever of a valve control, mounted at one end on a ball, especially for internal combustion engines, wherein a holder is mounted on the upper end of the valve shaft in such a manner as to axially lock and to prevent the lateral swinging of the rocking lever relative to the valve shaft.
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paper_title: Regularities and Singularities in Line Pictures

paper_content:

The main difficulty of Pattern Recognition is the interpretation of the iconic picture, the pixels, into primitive features. The interest of a feature classification as “regular” and “singular” is recalled and justified. These ideas are applied to line images, and particularly to handwritten words. On the graph representation of a word, a regular feature, the “axis,” is found; it allows to segment the word representation and to obtain a descriptive chain. Examples of such symbolic descriptions are given, with their interpretation as a word among a list of 25 words. The results are of the order of 87% success, 3% substitution on one handwriting. Preliminary results show that it may be extended to large categories of handwriting.
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paper_title: Global word shape processing in off-line recognition of handwriting

paper_content:

Off-line recognition of handwriting may be achieved using simplified profiles of word shapes. These profiles consist of approximations of the word's upper and lower contour. Training and recognition are based on n-gram extraction and identification. The lexicons used extend to 16,000 words.
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paper_title: Reading cursive handwriting by alignment of letter prototypes

paper_content:

We describe a new approach to the visual recognition of cursive handwriting. An effort is made to attain human-like performance by using a method based on pictorial alignment and on a model of the process of handwriting. The alignment approach permits recognition of character instances that appear embedded in connected strings. A system embodying this approach has been implemented and tested on five different word sets. The performance was stable both across words and across writers. The system exhibited a substantial ability to interpret cursive connected strings without recourse to lexical knowledge.
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paper_title: The Psychology of Reading

paper_content:

The pH, dispersibility and throw power of amine group-solubilized, epoxy resin-derived, cationic electrodepositable resins can be enhanced by the incorporation of primary amine groups into the resin molecule. The primary amine groups are incorporated by reacting certain polyamine compounds in which the primary amine groups are blocked by ketimine groups into the acid solubilizable, amine group-containing electrodepositable resins derived from epoxy group-containing resins.
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paper_title: Building a perception based model for reading cursive script

paper_content:

This paper presents a new perception based model for reading cursive script. We describe the organization of our pseudo-neuronal system and show the role of activation mechanism in perceiving and reading cursive script. We have introduced into our model some characteristics specific to cursive script. First, we use more appropriate features such as ascenders and descenders. Second, we deal with the ambiguity of letter location by introducing the concept of the fuzzy position. The location as well as the missing letters are deduced from the context (i.e. the word-letter lexicon). After implementation of our method, preliminary qualitative results have been obtained and are discussed. We are concentrating now on further formalizing and generalizing the proposed model on a larger data base.
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paper_title: Automatic reading of cursive scripts using human knowledge

paper_content:

Presents a model for reading cursive scripts which has an architecture inspired by a reading model and which is based on perceptual concepts. We limit the scope of our study to the off-line recognition of isolated cursive words. First of all, we justify why we chose McClelland & Rumelhart's (1981) reading model as the inspiration for our system. A brief resume/spl acute/ of the method's behavior is presented and the main originalities of our model are underlined. After this, we focus on the new updates added to the original system: a new baseline extraction module, a new feature extraction module and a new generation, validation and hypothesis insertion process. After implementation of our method, new results have been obtained on real images from a training set of 184 images, and a testing set of 100 images, and are discussed. We are concentrating now on validating the model using a larger database.
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paper_title: Extraction of key letters for cursive script recognition

paper_content:

Abstract This paper presents a new method for cursive script recognition based on the extraction of key letters which consist of the parts of the handwritten text which are singularities of handwriting. By processing the raw input data representing the handwritten text, the singular features: loops, ascenders, and descenders are first defined and determined. Based on them, the key letters are then extracted. This paper focuses on the extraction of potentially meaningful shapes, corresponding to visual letters, in the recognition of cursive handwriting. First the key letters of a cursive word are found, such as, b, f, p, q, F, T, etc., then a possible word based on these letters is selected. In the testing phase, there are 304 words from 74 cheques in our data base written by 40 writers. 276 words are correctly segmented, that is more than 90% suscessful segmentation. The problems leading to the missegmentation are identified for further improvement.
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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. >
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paper_title: A Multi-Level Perception Approach to Reading Cursive Script

paper_content:

Abstract Reading cursive script is the problem of transforming language from the iconic form of cursive human handwriting to one of ordinal representation. The problem involves elements of visual perception at one level of processing and those of language perception and understanding at a higher level. The problem is approached as one of multi-level perception in which a cursive script word image is transformed through a representational hierarchy. The levels are based on descriptions that use points, contours, features, letters, and words. Global control is hierarchical until an intermediate level after which it is heterarchical. A feature representation is generated bottom-up from the image using statistical dependencies between letters and features. Ratings for partially formed words are computed using a stack and a lexicon represented as a trie. Several heuristics for low- and intermediate-level processing for cursive script are introduced, including: reference-line finding using projection profile analysis, letter segmentation based on local lower contour minima and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features (e.g., middle loop, upper-zone stroke), and finding shape-oriented events. Two modes of learning are defined: initial training with user feedback and unsupervised adaptation to the writer. Experiments demonstrating the promise of the approach are described.
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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. >
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Title: Offline Cursive Script Word Recognition – A Survey

Section 1: Introduction
Description 1: Provide an overview of the progress in offline cursive word recognition, mention the common structure of word recognition systems, and outline the focus of the survey.

Section 2: Stages in a word recognition system
Description 2: Describe the global structure of a word recognition system and the different stages involved in the process, from preprocessing to post-processing.

Section 3: Segmentation-free methods
Description 3: Discuss various methods where word recognition is done without performing a meaningful segmentation first, including low-level, medium-level, and high-level feature extraction techniques and relevant algorithms.

Section 4: Specialized methods
Description 4: Review specific methods that use unique techniques for mapping observation sequences and mention examples of such specialized methods.

Section 5: Segmentation-based methods
Description 5: Describe methods where word recognition is based on segmenting the word image into primitive segments before recognition, and elaborate on the dynamic programming, shortest path variation, and HMM-based approaches.

Section 6: Perception-oriented methods
Description 6: Present methods that perform human-like reading of word images by identifying letters anywhere in the observation sequence and subsequently interpreting gaps.

Section 7: Discussion
Description 7: Discuss the observations regarding the current state of offline cursive word recognition, considerations for evaluating methods, and potential directions for future research.