Evaluation of Geometric Context Models for Handwritten Numeral String Recognition

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 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)
	2014
会议名称	2014 14th International Conference on Frontiers in Handwriting Recognition
会议日期	2014-9-1
会议地点	希腊克里特岛
摘要	Character 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.
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/19801
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	Institute of Institute of Automation,Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
推荐引用方式 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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