Abstract
In this paper, we propose different approaches for the segmentation of handwritten Devanagari word documents into constituent characters (or pseudo-characters). For accurate identification and segmentation of shiroreakha we exploited ShiroreakhaNet which is encoder-decoder based convolutional neural network. After, segmenting the shiroreakha structural patterns/properties are exploited for the segmentation of upper and lower modifiers. For the corroboration of the efficacy of the results, we collected dataset from different domains. Comparison is also performed with the state-of-the-art methods, and it was revealed that proposed approaches significantly perform better.
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Bhat, M.I., Sharada, B., Imran, M., Obaidullah, S. (2022). Automatic Segmentation of Handwritten Devanagari Word Documents Enabling Accurate Recognition. In: Chbeir, R., Manolopoulos, Y., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2021. Lecture Notes in Computer Science(), vol 13119. Springer, Cham. https://doi.org/10.1007/978-3-031-21517-9_8
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