Maximization of mutual information for offline Thai handwriting recognition.
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.
Nopsuwanchai
Roongroj
R
Information Technology Laboratory, Asahi, Kasei Corporation, AXT Maintower 22F, 3050 Okada, Atsugi Kanagawa, 243-0021, Japan. roongroj@cantab.net
Biem
Alain
A
Clocksin
William F
WF
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D004282:Documentation / Q000379:methods; D001330:Electronic Data Processing / Q000379:methods*; D006236:Handwriting; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D016013:Likelihood Functions; D015233:Models, Statistical; D009862:Online Systems; D010363:Pattern Recognition, Automated / Q000379:methods*; D013785:Thailand