Large scale on-line handwritten Chinese character recognition using improved syntactic pattern recognition

2002 ◽  
Author(s):  
K. Kuroda ◽  
K. Harada ◽  
M. Hagiwara
Keyword(s):  
Pattern Recognition ◽  
Character Recognition ◽  
Large Scale ◽  
Chinese Character ◽  
Syntactic Pattern Recognition ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
Syntactic Pattern ◽  
On Line

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.

On-Line Handwritten Chinese Character Recognition Directed by Components with Dynamic Templates

1998 ◽  
Vol 12 (01) ◽  
pp. 143-157 ◽  
Author(s):  
Xuhong Xiao ◽  
Ruwei Dai
Keyword(s):  
Computational Complexity ◽  
Character Recognition ◽  
Chinese Character ◽  
Chinese Characters ◽  
Partial Matching ◽  
Chinese Character Recognition ◽  
Handwritten Chinese Character Recognition ◽  
On Line ◽  
Segment Matching ◽  
Multiple Templates

Structural matching has been recognized as a promising approach for on-line Chinese character recognition. In order to reduce its great computational complexity and improve its performance, people have been seeking for ways to direct the matching of a whole character by the result of partial matching. In this paper, the authors proposed 45 basic components for 3,755 categories of daily-used Chinese characters to direct the stroke segment matching of whole characters. Since they are always located at either the beginning or the end of the stroke segment string of characters, these components are easy to be extracted and separated from other parts of a character. Besides, in our approach, the reference templates of these components are extracted dynamically from the corresponding segment string of characters when a specific matching is carried out. This strategy avoids building multiple templates for the components of the same kind but at different places of characters. The experiments show that the segment matching computation has been reduced greatly without reducing the correctness of matching.

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