Pavel Klavík, A. Cristiano I. Malossi, et al.
Philos. Trans. R. Soc. A
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. © 2006 IEEE.
Pavel Klavík, A. Cristiano I. Malossi, et al.
Philos. Trans. R. Soc. A
Miao Guo, Yong Tao Pei, et al.
WCITS 2011
Hannah Kim, Celia Cintas, et al.
IJCAI 2023
Joxan Jaffar
Journal of the ACM