Abstract
Script dependency of the Optical Character Recognition (OCR) systems is a huge obstacle for the digitalization of document images in a multi-script environment. Researchers around the world have developed various feature extraction and classification methodologies till date but mostly those are limited to bi-script and tri-script scenarios. The present work proposes an automatic two-stage framework for text-line based script recognition from the document images written in 12 Indic scripts. A misclassified text-line, at the first stage, is further examined by segmenting the same into its constituent words and the script recognition module is repeated on the obtained words. The pooled consequence of this two-stage framework helps to improve the overall accuracy of text-line level script classification.
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Singh, P.K., Mukhopadhyay, A., Sarkar, R., Nasipuri, M. (2018). Automatic Text-Line Level Handwritten Indic Script Recognition: A Two-Stage Framework. In: Satapathy, S., Tavares, J., Bhateja, V., Mohanty, J. (eds) Information and Decision Sciences. Advances in Intelligent Systems and Computing, vol 701. Springer, Singapore. https://doi.org/10.1007/978-981-10-7563-6_39
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DOI: https://doi.org/10.1007/978-981-10-7563-6_39
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