HANDPRINTED CHINESE CHARACTER RECOGNITION USING THE PROBABILITY DISTRIBUTION FEATURE

1994 ◽  
Vol 08 (05) ◽  
pp. 1241-1258 ◽  
Author(s):  
TZE FEN LI ◽  
SHIAW-SHIAN YU
Keyword(s):  
Probability Distribution ◽  
Character Recognition ◽  
Chinese Character ◽  
Recognition Rate ◽  
Bayes Rule ◽  
Chinese Characters ◽  
Two Dimensional ◽  
Shape Variation ◽  
Recognition Method ◽  
Chinese Character Recognition

A simplified Bayes rule is used to classify 5401 categories of handwritten Chinese characters. The main feature for the Bayes rule deals with the probability distribution of black pixels of a thinned character. Our idea is that each Chinese character indicated by the black pixels represents a probability distribution in a two-dimensional plane. Therefore, an unknown pattern is classified into one of 5401 different distributions by the Bayes rule. Since the handwritten character has an irregular shape variation, the whole character is normalized and then thinned. Finally, a transformation is used to spread the black pixels uniformly over the whole square plane, but it still keeps the relative positions of the original black pixels. The main feature gives an 88.65% recognition rate. In order to raise the recognition rate, 4 more subsidiary features are elaborately selected such that they are not affected much by the irregularly shaped variation. The 4 features raise the recognition rate to 93.43%. A 99.30% recognition rate is achieved if the top 10 categories of HCC are selected by our recognition method and 99.61% if the top 20 are selected.

scholarly journals Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship model

2010 ◽  
Vol 20 (4) ◽  
pp. 727-738 ◽  
Author(s):  
Yun Chang ◽  
Jia Lee ◽  
Omar Rijal ◽  
Syed Bakar
Keyword(s):  
Character Recognition ◽  
Functional Relationship ◽  
Chinese Character ◽  
Graph Transformation ◽  
Recognition Rate ◽  
Recognition System ◽  
Two Dimensional ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Relationship Model

Efficient online handwritten Chinese character recognition system using a two-dimensional functional relationship modelThis paper presents novel feature extraction and classification methods for online handwritten Chinese character recognition (HCCR). TheX-graph andY-graph transformation is proposed for deriving a feature, which shows useful properties such as invariance to different writing styles. Central to the proposed method is the idea of capturing the geometrical and topological information from the trajectory of the handwritten character using theX-graph and theY-graph. For feature size reduction, the Haar wavelet transformation was applied on the graphs. For classification, the coefficient of determination (R2p) from the two-dimensional unreplicated linear functional relationship model is proposed as a similarity measure. The proposed methods show strong discrimination power when handling problems related to size, position and slant variation, stroke shape deformation, close resemblance of characters, and non-normalization. The proposed recognition system is applied to a database with 3000 frequently used Chinese characters, yielding a high recognition rate of 97.4% with reduced processing time of 75.31%, 73.05%, 58.27% and 40.69% when compared with recognition systems using the city block distance with deviation (CBDD), the minimum distance (MD), the compound Mahalanobis function (CMF) and the modified quadratic discriminant function (MQDF), respectively. High precision rates were also achieved.

A research on an intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties

2020 ◽  
Vol 4 (4) ◽  
pp. 271-279
Author(s):  
Rui Guo
Keyword(s):  
Computer Technology ◽  
Character Recognition ◽  
Chinese Character ◽  
Research Process ◽  
Chinese Characters ◽  
Chinese Character Recognition ◽  
The World ◽  
Bronze Inscriptions ◽  
Intelligent Recognition ◽  
Functional Components

The intelligent recognition tool for bronze inscriptions of the Shang and Zhou dynasties—the “Shang Zhou Bronze Inscriptions Intelligent Mirror”—was successfully invented in Shanghai. This mirror, based on the computer technology of artificial intelligence (AI) image recognition and image retrieval, succeeds in automagical recognition of bronze inscriptions, both single letters and full texts. This research leads the trend of the AI recognition of Ancient Chinese characters and accumulates valuable experience for the development of inter-disciplinary research on Chinese character recognition. This essay emphasizes the importance of the bronze inscriptions of the Shang and Zhou dynasty database in the AI recognition of bronze inscriptions, introduces the functional components of this tool, and shares the whole research process in order to offer experience for the related research on AI recognition of other types of Ancient Chinese characters as well as ideographs in the world scope. “Shang Zhou Bronze Inscriptions Intelligent Mirror” as a tool for bronze inscription recognition also has room for improvement and support, and guidance from experts in similar areas is greatly welcomed.

On-Line Chinese Character Recognition Via a Representation of Spatial Relationships Between Strokes

1997 ◽  
Vol 11 (03) ◽  
pp. 329-357 ◽  
Author(s):  
Ju-Wei Chen ◽  
Suh-Yin Lee
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Spatial Relationship ◽  
Spatial Relations ◽  
Chinese Characters ◽  
Spatial Relationships ◽  
Matching Problem ◽  
Structural Rules ◽  
Chinese Character Recognition ◽  
On Line

Chinese characters are constructed by basic strokes based on structural rules. In handwritten characters, the shapes of the strokes may vary to some extent, but the spatial relations and geometric configurations of the strokes are usually maintained. Therefore these spatial relations and configurations could be regarded as invariant features and could be used in the recognition of handwritten Chinese characters. In this paper, we investigate the structural knowledge in Chinese characters and propose the stroke spatial relationship representation (SSRR) to describe Chinese characters. An On-Line Chinese Character Recognition (OLCCR) method using the SSRR is also presented. With SSRR, each character is processed and is represented by an attribute graph. The process of character recognition is thereby transformed into a graph matching problem. After careful analysis, the basic spatial relationship between strokes can be characterized into five classes. A bitwise representation is adopted in the design of the data structure to reduce storage requirements and to speed up character matching. The strategy of hierarchical search in the preclassification improves the recognition speed. Basically, the attribute graph model is a generalized character representation that provides a useful and convenient representation for newly added characters in an OLCCR system with automatic learning capability. The significance of the structural approach of character recognition using spatial relationships is analyzed and is proved by experiments. Realistic testing is provided to show the effectiveness of the proposed method.

scholarly journals Double-Sided Occluded Chinese Character Recognition Accuracy and Response Time for Design and Nondesign Educational Background

SAGE Open ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 215824401881006
Author(s):  
Ching-Chih Liao
Keyword(s):  
Response Time ◽  
Character Recognition ◽  
Visual Recognition ◽  
Chinese Character ◽  
Recognition Accuracy ◽  
Educational Status ◽  
Educational Background ◽  
Chinese Characters ◽  
Chinese Character Recognition ◽  
Educational Groups

This article investigates the influence of the position of occlusion, structural composition, and design educational status on Chinese character recognition accuracy and response time. Tsao and Liao conducted an experiment using 18 of the 4,000 most commonly used Chinese characters and suggested that the primary and secondary recognition features of a “single-sided” occluded Chinese character are the key radical (or initial strokes) and the key component (i.e., combination of strokes), respectively. The study concluded that right-side occluded characters require a shorter response time and yield more accurate recognition and that educational background does not significantly affect recognition accuracy and response time. The present study considered the same 18 Chinese characters and extended the work of Tsao and Liao by exploring accuracy rate and response time in design and nondesign educational groups for the recognition of “double-sided” occluded Chinese characters. The experimental results indicated that right-side occlusion (including both bottom-right and top-right occlusion) requires a shorter response time and yields more accurate recognition than left-side occlusion. These results agree with those of Tsao and Liao, who found that the key radical of a Chinese character is its key visual recognition feature. Even double-sided occlusion of Chinese characters does not affect the recognition outcome if the position of occlusion does not blur the key radical. Moreover, the participants majoring in design recognized the occluded Chinese characters more slowly than those with no educational background in design.

A Preclassification Method for Handwritten Chinese Character Recognition Via Fuzzy Rules and Seart Neural Net

1998 ◽  
Vol 12 (06) ◽  
pp. 743-761 ◽  
Author(s):  
Hahn-Ming Lee ◽  
Chin-Chou Lin ◽  
Jyh-Ming Chen
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Fuzzy Rules ◽  
Stage I ◽  
Chinese Characters ◽  
Neural Net ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Two Stages ◽  
Testing Set

In this paper, a method of character preclassification for handwritten Chinese character recognition is proposed. Since the number of Chinese characters is very large (at least 5401s for daily use), we employ two stages to reduce the candidates of an input character. In stage I, we extract the first set of primitive features from handwritten Chinese characters and use fuzzy rules to create four preclassification groups. The purpose in stage I is to reduce the candidates roughly. In stage II, we extract the second set of primitive features from handwritten Chinese characters and then use the Supervised Extended ART (SEART) as the classifier to generate preclassification classes for each preclassification group created in stage I. Since the number of characters in each preclassification class is smaller than that in the whole character set, the problem becomes simpler. In order to evaluate the proposed preclassification system, we use 605 Chinese character categories in the textbooks of elementary school as our training and testing data. The database used is HCCRBASE (provided by CCL, ITRI, Taiwan). In samples 1–100, we select the even samples as the training set, and the odd samples as the testing set. The characters of the testing set can be distributed into correct preclassification classes at a rate of 98.11%.

HANDWRITTEN CHINESE CHARACTER RECOGNITION WITH DIRECTIONAL DECOMPOSITION CELLULAR FEATURES

1998 ◽  
Vol 08 (04) ◽  
pp. 517-524 ◽  
Author(s):  
LIANWEN JIN ◽  
GANG WEI
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Minimum Distance ◽  
Chinese Character ◽  
Recognition Rate ◽  
Decomposition Methods ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
New Feature ◽  
Directional Decomposition

A new feature extraction approach based on elastic meshing and directional decomposition techniques for handwritten Chinese character recognition (HCCR) is proposed in this letter. It is found that decomposing a Chinese character into horizontal, vertical stroke, left slant and right slant directional sub-patterns is very helpful for feature extraction and recognition. Three kinds of decomposition methods are proposed. A minimum distance classifier is trained by 3755 categories of characters using the new features. Testing on a total of 37,550 untrained handwritten samples produces the recognition rate of 92.36%, showing the effectiveness of the proposed approach.

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