scholarly journals A Novel Blocked Winner Sequence Feature Extraction Method Based SOM For Large Set Chinese Character Recognition

Author(s):  
Lei Wang ◽  
Wenjuan Zhang ◽  
Lianming Wang
2013 ◽  
Vol 774-776 ◽  
pp. 1636-1641
Author(s):  
Ze Min Liu ◽  
Zhi Guo He ◽  
Yu Dong Cao

Feature extraction is very difficult for handwritten Chinese character because of large Chinese characters set, complex structure and very large shape variations. The recognition rate by currently used feature extraction methods is far from the requirements of the people. For this problem, a new supervised independent component analysis (SICA) algorithm based on J-divergence entropy is proposed for feature extraction, which can measure the difference between different categories. The scheme takes full advantage of good extraction local features capability and powerful capability to handle data with non-Gaussian distribution by ICA, and the extracted feature component and classification can be tightly combined. The experiments show that the feature extraction method based on SICA is superior to that of gradient-based and that of ICA.


Author(s):  
Htwe Pa Pa Win ◽  
Phyo Thu Thu Khine ◽  
Khin Nwe Ni Tun

This paper proposes a new feature extraction method for off-line recognition of Myanmar printed documents. One of the most important factors to achieve high recognition performance in Optical Character Recognition (OCR) system is the selection of the feature extraction methods. Different types of existing OCR systems used various feature extraction methods because of the diversity of the scripts’ natures. One major contribution of the work in this paper is the design of logically rigorous coding based features. To show the effectiveness of the proposed method, this paper assumed the documents are successfully segmented into characters and extracted features from these isolated Myanmar characters. These features are extracted using structural analysis of the Myanmar scripts. The experimental results have been carried out using the Support Vector Machine (SVM) classifier and compare the pervious proposed feature extraction method.


1998 ◽  
Vol 08 (04) ◽  
pp. 517-524 ◽  
Author(s):  
LIANWEN JIN ◽  
GANG WEI

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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