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Maximization of mutual information for offline Thai handwriting recognition

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3 Author(s)
Nopsuwanchai, R. ; Inf. Technol. Lab., Kasei Corp., Atsugi ; Biem, A. ; Clocksin, W.F.

This paper aims to improve the performance of an HMM-based offline Thai handwriting recognition system through discriminative training and the use of fine-tuned feature extraction methods. The discriminative training is implemented by maximizing the mutual information between the data and their classes. The feature extraction is based on our proposed block-based PCA and composite images, shown to be better at discriminating Thai confusable characters. We demonstrate significant improvements in recognition accuracies compared to the classifiers that are not discriminatively optimized

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:28 ,  Issue: 8 )