CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Evaluation of Geometric Context Models for Handwritten Numeral String Recognition
Wu YC(吴一超); Yin F(殷飞); Liu CL(刘成林)
Conference Name2014 14th International Conference on Frontiers in Handwriting Recognition
Conference Date2014-9-1
Conference Place希腊克里特岛
AbstractCharacter string recognition based on oversegmentation by integrating character classifier and context models has been demonstrated successful. Geometric context models characterizing the candidate character likeliness and betweencharacter relationship have shown benefits in several scripts but have not been evaluated in numeral string recognition. Compared with Chinese scripts mixed with alphanumeric and punctuation marks, numeral strings are less variant in character outline and between-character relationship. This study, via evaluating geometric context models used in Chinese handwritten text recognition, shows that geometric context is beneficial to handwritten numeral string recognition as well. Particularly, we propose an improved binary geometric model that combines single-character and between-character features such that the model functions like a bi-character classifier. Combining this binary geometric model with unary geometric model and character classifier, we obtain significant improvement of numeral string recognition performance on the NIST SD-19.
Document Type会议论文
AffiliationInstitute of Institute of Automation,Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wu YC,Yin F,Liu CL. Evaluation of Geometric Context Models for Handwritten Numeral String Recognition[C],2014.
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