The Optimum Class-Selective Rejection Rule
IEEE Transactions on Pattern Analysis and Machine Intelligence
Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Nearest prototype classification: clustering, genetic algorithms, or random search?
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper we present a novel multistage classification strategy for handwriting Chinese character recognition. In training phase, we search for the most representative prototypes and divide the whole class set into several groups using prototype-based clustering. These groups are extended by nearest-neighbor rule and their centroids are used for coarse classification. In each group, we extract the most discriminative feature by local linear discriminant analysis and design the local classifier. The above-mentioned prototypes and centroids are optimized by a hierarchical learning vector quantization. In recognition phase, we first find the nearest group of the unknown sample, and then get the desired class label through the local classifier. Experiments have been implemented on CASIA database and the results show that the proposed method reaches a reasonable tradeoff between efficiency and accuracy.