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Maximization of Mutual Information for Offline Thai Handwriting Recognition
August 2006 (vol. 28 no. 8)
pp. 1347-1351
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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Index Terms:
Character recognition, Hidden Markov Model, discriminative training, PCA, feature extraction, Thai handwriting recognition.
Citation:
Roongroj Nopsuwanchai, Alain Biem, William F. Clocksin, "Maximization of Mutual Information for Offline Thai Handwriting Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1347-1351, Aug. 2006, doi:10.1109/TPAMI.2006.167
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