[1]MA Long-long,LIU Cheng-lin.On-line handwritten Chinese character recognition using statistical radical models[J].CAAI Transactions on Intelligent Systems,2010,5(5):385-391.[doi:10.3969/j.issn.1673-4785.2010.05.002]
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On-line handwritten Chinese character recognition using statistical radical models

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 5 Page number: 385-391 Column: 学术论文—机器感知与模式识别 Public date: 2010-10-25
Title:
On-line handwritten Chinese character recognition using statistical radical models
Author(s):
MA Long-long;  LIU Cheng-lin
(National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, Beijing 100190, China)
Keywords:
on-line handwritten Chinese character recognition;  statistical radical model;  hierarchical structure;  over segmentation;  path search;  radical recognition
CLC:
TP391.4
DOI:
10.3969/j.issn.1673-4785.2010.05.002
Abstract:
The hierarchical radical structure of Chinese characters can be explored to reduce the number of parameters in character recognition, as well as to improve the generalization ability and adaptability. However, the segmentation of radicals from characters has long been a difficult problem. A new radical-based approach for online handwritten Chinese character recognition was proposed. The approach integrated appearance-based radical recognition and geometric context into a principled framework using a hierarchical character-radical dictionary to guide radical segmentation and recognition during the path search process for the purpose of increasing the accuracy of radical segmentation. The parameters of statistical radical models were estimated in embedded learning. To overcome the connection of strokes between radicals, corner points were detected to extract sub-strokes. For character recognition, two dictionary representation schemes and accordingly different search algorithms were used. The effectiveness of the proposed approach has been demonstrated on Chinese characters of left-right and up-down structures.
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