Abstract
recognizing lines of handwritten text is a difficult task. Most recent evolution in the field has been made either through better-quality pre processing or through advances in language modeling. Most systems rely on hidden Markov models that have been used for decades in speech and handwriting recognition. So an approach is proposed in this paper which is based on a type of recurrent neural network, in particularly designed for sequence labeling tasks where the data is hard to segment and contains long-range bidirectional interdependencies. Recurrent neural networks (RNN) have been successfully applied for recognition of cursive handwritten documents, in scripts like English and Arabic. A regular recurrent neural network (RNN) is extended to a bidirectional recurrent neural network (BRNN).
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Oval, S.G., Shirawale, S. (2015). Recognizing Handwritten Devanagari Words Using Recurrent Neural Network. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-12012-6_45
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DOI: https://doi.org/10.1007/978-3-319-12012-6_45
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