Maximization of mutual information for offline Thai handwriting recognition

Roongroj Nopsuwanchai, Alain Biem, William Clocksin

    Research output: Contribution to journalArticlepeer-review

    24 Citations (Scopus)


    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
    Original languageEnglish
    Pages (from-to)1347-1351
    JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
    Issue number8
    Publication statusPublished - 2006


    Dive into the research topics of 'Maximization of mutual information for offline Thai handwriting recognition'. Together they form a unique fingerprint.

    Cite this