Unconstrained Bangla online handwriting recognition based on MLP and SVM
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
Attention-Feedback Based Robust Segmentation of Online Handwritten Isolated Tamil Words
ACM Transactions on Asian Language Information Processing (TALIP)
Levenshtein distance metric based holistic handwritten word recognition
Proceedings of the 4th International Workshop on Multilingual OCR
Bag-of-features HMMs for segmentation-free Bangla word spotting
Proceedings of the 4th International Workshop on Multilingual OCR
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For automatic recognition of Bangla script, only a few studies are reported in the literature, which is in contrast to the role of Bangla as one of the world's major scripts. In this paper we present a new approach to online Bangla handwriting recognition and one of the first to consider cursively written words instead of isolated characters. Our method uses a sub-stroke level feature representation of the script and a writing model based on hidden Markov models. As for the latter an appropriate internal structure is crucial, we investigate different approaches to defining model structures for a highly compositional script like Bangla. In experimental evaluations of a writer independent Bangla word recognition task we show that the use of context-dependent sub-word units achieves quite promising results and significantly outperforms alternatively structured models.