Thinning and stroke segmentation for handwritten Chinese character recognition

Firstly, a thinning technique by means of stroke tracking is proposed. The method is considered to preserve the straightness of strokes and the length, which is useful for the stroke segmentation procedure on the recognition of handwritten Chinese characters.Secondly, a method for stroke segmentation, i.c. a way of breaking down a character to a set of consecutive partial strokes, is proposed, which works well owing to the favourable properties of the proposed thinning technique. The method consists of five procedures: extraction of feature pixels, calculation of stroke directions, piecewise linear representation of strokes, unification of intersections and extraction of the consecutive partial strokes.     

A novel stroke-based feature extraction for handwritten Chinese character recognition

A stroke-based approach to extract skeletons and structural features for handwritten Chinese character recognition is proposed. We first determine stroke directions based on the directional run-length information of binary character patterns. According to the stroke directions and their adjacent relationships, we split strokes into stroke and fork segments, and then extract the skeletons of the stroke segments called skeleton segments. After all skeleton segments are extracted, fork segments are processed to find the fork points and fork degrees. Skeleton segments that touch a fork segment are connected at the fork point, and all connected skeleton segments form the character skeleton. According to the extracted skeletons and fork points, we can extract primitive strokes and stroke direction maps for recognition. A simple classifier based on the stroke direction map is presented to recognize regular and rotated characters to verify the ability of the proposed feature extraction for handwritten Chinese character recognition. Several experiments are carried out, and the experimental results show that the proposed approach can easily and effectively extract skeletons and structural features, and works well for handwritten Chinese character recognition.     

Preprocessing for Chinese character recognition and global classification of handwritten Chinese characters

Techniques for calculating the stroke directions of thinned binary characters and for detecting the intersections and end points of strokes by means of pattern matching and weighting method are proposed as a preprocessing of handwritten Chinese character recognition. We also propose a method for global classification of handwritten Chinese characters by means of projection profiles of strokes and show that the method is available for the Chinese characters written in the square style.     

SVM-based hierarchical architectures for handwritten Bangla character recognition

We propose support vector machine (SVM) based hierarchical classification schemes for recognition of handwritten Bangla characters. A comparative study is made among multilayer perceptron, radial basis function network and SVM classifier for this 45 class recognition problem. SVM classifier is found to outperform the other classifiers. A fusion scheme using the three classifiers is proposed which is marginally better than SVM classifier. It is observed that there are groups of characters having similar shapes. These groups are determined in two different ways on the basis of the confusion matrix obtained from SVM classifier. In the former, the groups are disjoint while they are overlapped in the latter. Another grouping scheme is proposed based on the confusion matrix obtained from neural gas algorithm. Groups are disjoint here. Three different two-stage hierarchical learning architectures (HLAs) are proposed using the three grouping schemes. An unknown character image is classified into a group in the first stage. The second stage recognizes the class within this group. Performances of the HLA schemes are found to be better than single stage classification schemes. The HLA scheme with overlapped groups outperforms the other two HLA schemes.     

Off-line handwritten Korean character recognition based on stroke extraction and representation

In this paper, we propose an off-line recognition method for handwritten Korean characters based on stroke extraction and representation. To recognize handwritten Korean characters, it is required to extract strokes and stroke sequence to describe an input of two-dimensional character as one-dimensional representation. We define 28 primitive strokes to represent characters and introduce 300 stroke separation rules to extract proper strokes from Korean characters. To find a stroke sequence, we use stroke code and stroke relationship between consecutive strokes. The input characters are recognized by using character recognition trees. The proposed method has been tested for the most frequently used 1000 characters by 400 different writers and showed recognition rate of 94.3%.     

Methods for adaptive combination of classifiers with application to recognition of handwritten characters

This paper discusses two techniques for improving the recognition accuracy for online handwritten character recognition: committee classification and adaptation to the user. Combining classifiers is a common method for improving recognition performance. Improvements are possible because the member classifiers may make different errors. Much variation exists in handwritten characters, and adaptation is one feasible way of dealing with such variation. Even though adaptation is usually performed for single classifiers, it is also possible to use adaptive committees. Some novel adaptive committee structures, namely, the dynamically expanding context (DEC), modified current best learning (MCBL), and class-confidence critic combination (CCCC), are presented and evaluated. They are shown to be able to improve on their member classifiers, with CCCC offering the best performance. Also, the effect of having either more or less diverse sets of member classifiers is considered.Received: 17 September 2002, Accepted: 22 October 2002, Published online: 4 July 2003     

Unconstrained handwritten character recognition based on fuzzy logic

This paper presents an innovative approach called box method for feature extraction for the recognition of handwritten characters. In this method, the binary image of the character is partitioned into a fixed number of subimages called boxes. The features consist of vector distance (γ) from each box to a fixed point. To find γ the vector distances of all the pixels, lying in a particular box, from the fixed point are calculated and added up and normalized by the number of pixels within that box. Here, both neural networks and fuzzy logic techniques are used for recognition and recognition rates are found to be around 97 percent using neural networks and 98 percent using fuzzy logic. The methods are independent of font, size and with minor changes in preprocessing, it can be adopted for any language.     

Holistic recognition of handwritten character pairs

Researchers have thus far focused on the recognition of alpha and numeric characters in isolation as well as in context. In this paper we introduce a new genre of problems where the input pattern is taken to be a pair of characters. This adds to the complexity of the classification task. The 10 class digit recognition problem is now transformed into a 100 class problem where the classes are {00,…, 99}. Similarly, the alpha character recognition problem is transformed to a 26×26 class problem, where the classes are {AA,…, ZZ}. If lower-case characters are also considered the number of classes increases further. The justification for adding to the complexity of the classification task is described in this paper. There are many applications where the pairs of characters occur naturally as an indivisible unit. Therefore, an approach which recognizes pairs of characters, whether or not they are separable, can lead to superior results. In fact, the holistic method described in this paper outperforms the traditional approaches that are based on segmentation. The correct recognition rate on a set of US state abbreviations and digit pairs, touching in various ways, is above 86%.     

Performance evaluation of pattern classifiers for handwritten character recognition

This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a statistical classifier (modified quadratic discriminant function, MQDF), three neural classifiers, and an LVQ (learning vector quantization) classifier. They are efficient in that high accuracies can be achieved at moderate memory space and computation cost. The performance is measured in terms of classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance. The outlier resistance of neural classifiers is enhanced by training with synthesized outlier data. The classifiers are tested on a large data set extracted from NIST SD19. As results, the test accuracies of the evaluated classifiers are comparable to or higher than those of the nearest neighbor (1-NN) rule and regularized discriminant analysis (RDA). It is shown that neural classifiers are more susceptible to small sample size than MQDF, although they yield higher accuracies on large sample size. As a neural classifier, the polynomial classifier (PC) gives the highest accuracy and performs best in ambiguity rejection. On the other hand, MQDF is superior in outlier rejection even though it is not trained with outlier data. The results indicate that pattern classifiers have complementary advantages and they should be appropriately combined to achieve higher performance. Received: July 18, 2001 / Accepted: September 28, 2001     

Eigen-deformations for elastic matching based handwritten character recognition

Deformations in handwritten characters have category-dependent tendencies. In this paper, the estimation and the utilization of such tendencies called eigen-deformations are investigated for the better performance of elastic matching based handwritten character recognition. The eigen-deformations are estimated by the principal component analysis of actual deformations automatically collected by the elastic matching. From experimental results it was shown that typical deformations of each category can be extracted as the eigen-deformations. It was also shown that the recognition performance can be improved significantly by using the eigen-deformations for the detection of overfitting, which is the main cause of the misrecognition in the elastic matching based recognition methods.     

Learning stochastic edit distance: Application in handwritten character recognition

Many pattern recognition algorithms are based on the nearest-neighbour search and use the well-known edit distance, for which the primitive edit costs are usually fixed in advance. In this article, we aim at learning an unbiased stochastic edit distance in the form of a finite-state transducer from a corpus of (input, output) pairs of strings. Contrary to the other standard methods, which generally use the Expectation Maximisation algorithm, our algorithm learns a transducer independently on the marginal probability distribution of the input strings. Such an unbiased way to proceed requires to optimise the parameters of a conditional transducer instead of a joint one. We apply our new model in the context of handwritten digit recognition. We show, carrying out a large series of experiments, that it always outperforms the standard edit distance.     

手写混合字符集识别的多特征多级分类器设计

针对常用的银行汉字和阿拉伯数字混合字符集的识别,提出了依据不同的分类要求，分别选取不同的分类特征，并采用先聚类再用多层感知器(MLP)神经网络分类的多级分类器进行识别的设计方法。实验结果表明，该方法用于手写体混合字符集的识别是行之有效的。     

High accuracy handwritten Chinese character recognition using LDA-based compound distances

To improve the accuracy of handwritten Chinese character recognition (HCCR), we propose linear discriminant analysis (LDA)-based compound distances for discriminating similar characters. The LDA-based method is an extension of previous compound Mahalanobis function (CMF), which calculates a complementary distance on a one-dimensional subspace (discriminant vector) for discriminating two classes and combines this complementary distance with a baseline quadratic classifier. We use LDA to estimate the discriminant vector for better discriminability and show that under restrictive assumptions, the CMF is a special case of our LDA-based method. Further improvements can be obtained when the discriminant vector is estimated from higher-dimensional feature spaces. We evaluated the methods in experiments on the ETL9B and CASIA databases using the modified quadratic discriminant function (MQDF) as baseline classifier. The results demonstrate the superiority of LDA-based method over the CMF and the superiority of discriminant vector learning from high-dimensional feature spaces. Compared to the MQDF, the proposed method reduces the error rates by factors of over 26%.     

Zoning methods for handwritten character recognition: A survey

This paper presents a survey on zoning methods for handwritten character recognition. Through the analysis of the relevant literature in the field, the most valuable zoning methods are presented in terms of both topologies and membership functions. Throughout the paper, diverse zoning topologies are presented based on both static and adaptive approaches. Concerning static approaches, uniform and non-uniform zoning strategies are discussed. When adaptive zonings are considered, manual and automatic strategies for optimal zoning design are illustrated as well as the most appropriate zoning representation techniques. In addition, the role of membership functions for zoning-based classification is highlighted and the diverse approaches to membership function selection are presented. Concerning global membership functions, the paper introduces order-based approaches as well as fuzzy approaches using border-based and ranked-based fuzzy membership values. Concerning local membership functions, the recent parameter-based approaches are described, in which the optimal membership-function is selected for each zone of the zoning method. Finally, a comparative analysis on the performance of zoning methods is presented and the most interesting approaches are focused on in terms of topology design and membership function selection. A list of selected references is provided as a useful tool for interested researchers working in the field.     

On-line computer recognition of proposed standard ANSI(USASI) handprinted characters

A robust real-time system for recognition of handprinted characters of the upper case English alphabet is described. The basic system is suited to implementation on small computers and has been designed to accept characters conforming to the stroke types and sequences suggested by a proposed ANSI(USASI) standard. Experiments with 2340 samples from 10 untrained subjects yielded an overall character recognition accuracy of 98.3%. The system is quite robust with respect to size and stylistic variations. The robustness and real-time operation of the system are largely attributed to the preprocessing and stroke identification techniques developed, which include a new two-stage syntactic classifier for the identification of curvilinear strokes.     

Combining multiple classifiers based on third-order dependency for handwritten numeral recognition

Storing and estimating high order probability distribution of classifiers and class labels is exponentially complex and unmanageable, so we rely on an approximation scheme using the dependency. As an extension of the second-order dependency approach, the probability distribution is optimally approximated by the third-order dependency and then multiple classifiers are combined by such third-order dependency approximation.     

An overview of character recognition methodologies

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

Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition

The polynomial classifier (PC) that takes the binomial terms of reduced subspace features as inputs has shown superior performance to multilayer neural networks in pattern classification. In this paper, we propose a class-specific feature polynomial classifier (CFPC) that extracts class-specific features from class-specific subspaces, unlike the ordinary PC that uses a class-independent subspace. The CFPC can be viewed as a hybrid of ordinary PC and projection distance method. The class-specific features better separate one class from the others, and the incorporation of class-specific projection distance further improves the separability. The connecting weights of CFPC are efficiently learned class-by-class to minimize the mean square error on training samples. To justify the promise of CFPC, we have conducted experiments of handwritten digit recognition and numeral string recognition on the NIST Special Database 19 (SD19). The digit recognition task was also benchmarked on two standard databases USPS and MNIST. The results show that the performance of CFPC is superior to that of ordinary PC, and is competitive with support vector classifiers (SVCs).     

Combining additive input noise annealing and pattern transformations for improved handwritten character recognition

Two problems that burden the learning process of Artificial Neural Networks with Back Propagation are the need of building a full and representative learning data set, and the avoidance of stalling in local minima. Both problems seem to be closely related when working with the handwritten digits contained in the MNIST dataset. Using a modest sized ANN, the proposed combination of input data transformations enables the achievement of a test error as low as 0.43%, which is up to standard compared to other more complex neural architectures like Convolutional or Deep Neural Networks.