Precise candidate selection for large character set recognition by confidence evaluation

CASIA OpenIR > 09年以前成果

	Precise candidate selection for large character set recognition by confidence evaluation
	Cheng-Lin Liu; Masaki Nakagawa
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
	2000
卷号	22 期号:6 页码:636-642
摘要	This paper proposes a precise candidate selection method for large character set recognition by confidence evaluation of distance-based classifiers. The proposed method is applicable to a wide variety of distance metrics and experiments on Euclidean distance and city block distance have achieved promising results. By confidence evaluation, the distribution of distances is analyzed to derive the probabilities of classes in two steps: output probability evaluation and input probability inference. Using the input probabilities as confidences, several selection rules have been tested and the rule that selects the classes with high confidence ratio to the first rank class produced best results. The experiments were implemented on the ETL9B database and the results show that the proposed method selects about one-fourth as many candidates with accuracy preserved compared to the conventional method that selects a fixed number of candidates
关键词	None
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/13404
专题	09年以前成果
推荐引用方式 GB/T 7714	Cheng-Lin Liu,Masaki Nakagawa. Precise candidate selection for large character set recognition by confidence evaluation[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2000,22(6):636-642.
APA	Cheng-Lin Liu,&Masaki Nakagawa.(2000).Precise candidate selection for large character set recognition by confidence evaluation.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,22(6),636-642.
MLA	Cheng-Lin Liu,et al."Precise candidate selection for large character set recognition by confidence evaluation".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 22.6(2000):636-642.