Recognition Of Handprinted Chinese Characters Research Articles

Recognition of hand-printed Chinese characters using decision trees/machine learning C4.5 system

Recognition of Chinese characters has been an area of major interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This paper presents a new technique for the recognition of hand-printed Chinese characters using the C4.5 machine learning system. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variation in writing styles. The paper discusses Chinese character recognition using theHough transform for feature extraction and C4.5 system. The system was tested with 900 characters written by different writers from poor to acceptable quality (each character has 40 samples) and the rate of recognition obtained was 84%.

Adaptive recognition of Chinese characters: imitation of psychological process in machine recognition

This paper is focused on imitation of human psychological process in machine recognition of Chinese characters. Some results of research on human Chinese character recognition have been discussed and unified into a compound mechanism with an adaptive and self-developing nature. A machine imitation model has been proposed for Chinese character recognition with different routines. By some simplification but with the crucial feature of the model being retained, an experimental system for handprinted Chinese character recognition based on the novel concept has been built. Experimental results have shown that the associated routines continuously improve their performance during their work even after supervised training is halted. The routine of the global pattern approach eventually learns most of the classes and the recognition process gradually shifts from the subpattern approach to the global pattern approach.

Recognition of handprinted Chinese characters by constrained graph matching

A model-based handwritten Chinese character recognition (HCCR) system is proposed. The characters are represented by attributed relational graphs (ARG) using strokes as ARG vertices. A number of vector relational attributes are also used in the representation to improve the performance of the translation and scale invariant and rotation sensitive recognition system. Since the ETL-8 database is very noisy and broken strokes are commonly encountered, a suitable homomorphic energy function is proposed that allows the segments of a broken stroke of a test character to be matched to the corresponding model stroke. The homomorphic ARG matching energy is minimised using the self-organising Hopfield neural networks [1] [Suganthan, P.N., Teoh, E.K., Mital, D.P., A self-organising Hopfield network for attributed relational graph matching, Image and Vision Computing, 13(1) (1995) 61–73]. An effective formulation is introduced to determine the matching score. The formulation does not penalise the matching scores of test characters with broken strokes. Experiments were performed with 100 classes of characters in the ETL-8 database and 98.9% recognition accuracy has been achieved.

Machine Recognition of Hand-Printed Chinese Characters

Machine Recognition of Hand-Printed Chinese Characters

Machine Recognition of Hand-Printed Chinese Characters

The recognition of Chinese characters has been an area of great interest for many years, and a large number of research papers and reports have already been published in this area. There are several major problems with Chinese character recognition: Chinese characters are distinct and ideographic, the character size is very large and many structurally similar characters exist in the character set. Thus, classification criteria are difficult to generate. This article presents a new technique for the recognition of hand-printed Chinese characters using statistical pattern classification. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct, and difficult to make tolerant to variation in writing styles. The article also discusses Chinese character recognition using dominant point feature extraction, and statistical pattern classification. The system was tested with 500 characters (each character has 40 samples), and the rate of recognition obtained was 84.45%. This strongly supports the usefulness of the proposed measures for Chinese character classification.

LEARNING TO RECOGNIZE HAND-PRINTED CHINESE CHARACTERS USING INDUCTIVE LOGIC PROGRAMMING

Recognition of Chinese characters has been a major interest of researchers for many years, and a large number of research papers and reports have already been published in this area. There are several major problems: Chinese characters are distinct and ideographic, the character size is very large and a lot of structurally similar characters exist in the character set. Thus, classification criteria are difficult to find. This paper presents a new technique for the recognition of hand-printed Chinese characters using machine learning. Conventional methods have relied on hand-constructed dictionaries which are tedious to construct and difficult to make tolerant to variations in writing styles. The advantages of machine learning are twofold: it can generalize over the large degree of variations between writing styles and recognition rules can be constructed by example. The paper also describes three methods of feature extraction for Chinese character recognition: regular expression, dominant point and modified Hough transform. These methods are then compared in terms of accuracy and efficiency.

Recognition of handprinted chinese characters via stroke relaxation

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

Representation and recognition of handprinted Chinese characters by string-matching

This paper describes an optical recognition system for the handprinted Chinese characters. It is based on a novel string representation method and an inductive learning scheme that allows flexible (or elastic) representation and matching of unknown character instances. The system scans a character instance from four different views to obtain its peripheral segment information. A string representation is designed for representing the peripheral information at each of the four views. This representation can be generalized to represent the variations in different instances of a character by using an inductive learning algorithm. A clustering algorithm is developed to group the learned representation of characters into clusters in a hierarchical tree structure. Finally, a two-stage recognition process based on the developed representation is described. Experimental results demonstrate that high recognition rates can be obtained using the developed method.

Correction to "Fuzzy-attribute graph with application to Chinese character recognition"

In everyday life, many properties or concepts we encumber are fuzzy in nature. To include those fuzzy properties in solving some types of problems, we have extended the attributed graph to fuzzy-attribute graph (FAG). With such extension, equality of attributes can no longer be used when matching of FAG's is considered, as equality of two fuzzy sets is too strigent a condition. In the paper, fuzzy-attribute graph is formally defines and a new measure for matching two FAG's is suggested. Yhe new measure has its interpretation under fuzzy logic. The model is applied to the recognition of handprinted Chinese characters and the result is presented.

Fuzzy-attribute graph with application to Chinese character recognition

To include fuzzy properties in solving some types of problems, the attributes graph is extended to provide a fuzzy-attribute graph (FAG). With such an extension, equality of attributes can no longer be used when matching of FAGs is considered, as equality of two fuzzy sets is too stringent a condition. A measure for matching two FAGs is suggested. The measure has its interpretation in fuzzy logic. The result of applying the model to the recognition of handprinted Chinese characters is presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

On speeding candidate selection in handprinted chinese character recognition

A method of selecting candidate categories to speed handprinted Chinese character recognition is proposed. This method is set up in the pre-stage of conventional candidate selection and reduces the large candidate selection time which is due to a vast number of Chinese categories. Its performance tested on a Chinese character database is also shown.

2-D EAG method for the recognition of hand-printed Chinese characters

A method, called Two- Dimensional Extended Attribute Grammars (2-D EAGs), for the recognition of hand- printed Chinese characters is presented. This method uses directly two dimensional information, and provides a scheme for dealing with various kinds of specific cases in a uniform way. In this method, components are drawn in guided and redundant way and reductions are made level by level just in accordance with the component combination relations of Chinese characters. The method provides also polysemous grammars, coexisting grammars and structure inferrings which constrain redundant recognition by comparison among similar characters or components and greatly increase the tolerance ability to distortion.

Two-dimensional extended attribute grammar method for the recognition of hand-printed chinese characters

This paper introduces the design and realization of two-dimensional extended attribute grammar method (2D EAG for short) for the recognition of hand-printed Chinese characters. In this method, 2D EAGs are used to carry both overall character shape information and local features of samples, and to conduct top-down matching and bottom-up reduction. The strategies of separating element drawing and grammar processing, autonomous component drawing, two dimensional redundant reduction, and structure analogizing are proposed.

On machine recognition of hand-printed Chinese characters by feature relaxation

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

Research on Machine Recognition of Handprinted Characters

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

The automatic recognition of handprinted Chinese characters — A method of extracting an ordered sequence of strokes

In this paper, a method of recognizing handprinted Chinese characters by extracting and composing an ordered sequence of strokes is presented. A computer simulation for three hundred Chinese characters has been done, and the results are satisfactory.