scholarly journals High Accuracy Handwritten Chinese Character Recognition Using Quadratic Classifiers with Discriminative Feature Extraction

2006 ◽  
Author(s):  
Cheng-Lin Liu
Keyword(s):  
Feature Extraction ◽  
Character Recognition ◽  
Chinese Character ◽  
High Accuracy ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Discriminative Feature

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.

The Handwritten Chinese Character Recognition Uses Convolutional Neural Networks with the GoogLeNet

2019 ◽  
Vol 33 (11) ◽  
pp. 1940016 ◽  
Author(s):  
Ning Bi ◽  
Jiahao Chen ◽  
Jun Tan
Keyword(s):  
Character Recognition ◽  
Large Scale ◽  
Visual Recognition ◽  
Chinese Character ◽  
State Of The Art ◽  
High Accuracy ◽  
Final Version ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Insight Into

With the outstanding performance in 2014 at the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14), an effective convolutional neural network (CNN) model named GoogLeNet has drawn the attention of the mainstream machine learning field. In this paper we plan to take an insight into the application of the GoogLeNet in the Handwritten Chinese Character Recognition (HCCR) on the database HCL2000 and CASIA-HWDB with several necessary adjustments and also state-of-the-art improvement methods for this end-to-end approach. Through the experiments we have found that the application of the GoogLeNet for the Handwritten Chinese Character Recognition (HCCR) results into significant high accuracy, to be specific more than 99% for the final version, which is encouraging for us to further research.

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