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Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation
June 2000 (vol. 22 no. 6)
pp. 636-642

Abstract—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.

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Index Terms:
Handwritten character recognition, large character set, candidate selection, confidence evaluation, Bayesian inference.
Citation:
Cheng-Lin Liu, Masaki Nakagawa, "Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 636-642, June 2000, doi:10.1109/34.862202
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