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.

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 HYBRID POST-PROCESSING SYSTEM FOR OFFLINE HANDWRITTEN CHINESE CHARACTER RECOGNITION BASED ON A STATISTICAL LANGUAGE MODEL

2005 ◽  
Vol 19 (03) ◽  
pp. 415-428 ◽  
Author(s):  
RUIFENG XU ◽  
DANIEL S. YEUNG ◽  
DAMING SHI
Keyword(s):  
Character Recognition ◽  
Chinese Character ◽  
Language Model ◽  
Recognition Rate ◽  
Processing System ◽  
Misclassification Rate ◽  
Post Processing ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Word Lattice

This paper presents a post-processing system for improving the recognition rate of a Handwritten Chinese Character Recognition (HCCR) device. This three-stage hybrid post-processing system reduces the misclassification and rejection rates common in the single character recognition phase. The proposed system is novel in two respects: first, it reduces the misclassification rate by applying a dictionary-look-up strategy that bind the candidate characters into a word-lattice and appends the linguistic-prone characters into the candidate set; second, it identifies promising sentences by employing a distant Chinese word BI-Gram model with a maximum distance of three to select plausible words from the word-lattice. These sentences are then output as the upgraded result. Compared with one of our previous works in single Chinese character recognition, the proposed system improves absolute recognition rates by 12%.

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