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Precise candidate selection for large character set recognition by confidence evaluation
Cheng-Lin Liu; Masaki Nakagawa
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2000
Volume22Issue:6Pages:636-642
AbstractThis 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
KeywordNone
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/13404
Collection09年以前成果
Recommended Citation
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.
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