Handwritten Chinese Text Recognition by Integrating Multiple Contexts

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

	Handwritten Chinese Text Recognition by Integrating Multiple Contexts
	Wang, Qiu-Feng; Yin, Fei; Liu, Cheng-Lin
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2012-08-01
卷号	34 期号:8 页码:1469-1481
文章类型	Article
摘要	This paper presents an effective approach for the offline recognition of unconstrained handwritten Chinese texts. Under the general integrated segmentation-and-recognition framework with character oversegmentation, we investigate three important issues: candidate path evaluation, path search, and parameter estimation. For path evaluation, we combine multiple contexts (character recognition scores, geometric and linguistic contexts) from the Bayesian decision view, and convert the classifier outputs to posterior probabilities via confidence transformation. In path search, we use a refined beam search algorithm to improve the search efficiency and, meanwhile, use a candidate character augmentation strategy to improve the recognition accuracy. The combining weights of the path evaluation function are optimized by supervised learning using a Maximum Character Accuracy criterion. We evaluated the recognition performance on a Chinese handwriting database CASIA-HWDB, which contains nearly four million character samples of 7,356 classes and 5,091 pages of unconstrained handwritten texts. The experimental results show that confidence transformation and combining multiple contexts improve the text line recognition performance significantly. On a test set of 1,015 handwritten pages, the proposed approach achieved character-level accurate rate of 90.75 percent and correct rate of 91.39 percent, which are superior by far to the best results reported in the literature.
关键词	Handwritten Chinese Text Recognition Confidence Transformation Geometric Models Language Models Refined Beam Search Candidate Character Augmentation Maximum Character Accuracy Training
WOS标题词	Science & Technology ; Technology
关键词[WOS]	POST-PROCESSING SYSTEM ; CHARACTER-RECOGNITION ; CLASSIFIER COMBINATION ; OFFLINE RECOGNITION ; SCRIPT RECOGNITION ; SEGMENTATION ; MODEL ; ALGORITHMS ; STRINGS
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000305188500002
引用统计	被引频次：109[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3073
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	Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Wang, Qiu-Feng,Yin, Fei,Liu, Cheng-Lin. Handwritten Chinese Text Recognition by Integrating Multiple Contexts[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2012,34(8):1469-1481.
APA	Wang, Qiu-Feng,Yin, Fei,&Liu, Cheng-Lin.(2012).Handwritten Chinese Text Recognition by Integrating Multiple Contexts.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,34(8),1469-1481.
MLA	Wang, Qiu-Feng,et al."Handwritten Chinese Text Recognition by Integrating Multiple Contexts".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 34.8(2012):1469-1481.