Abstract
On-line handwriting recognition has seen major strides in the past years, especially with the advent of deep learning techniques. Recent work has seen the usage of deep networks for sequential classification of unconstrained handwriting recognition task. However, the recognition of “Hinglish” language faces various unseen problems. Hinglish is a portmanteau of Hindi and English, involving frequent code-switching between the two languages. Millions of Indians use Hinglish as a primary mode of communication, especially across social media. However, being a colloquial language, Hinglish does not have a fixed rule set for spelling and grammar. Auto-correction is an unsuitable solution as there is no correct form of the word, and all the multiple phonetic variations are valid. Unlike the advantage that keyboards provide, recognizing handwritten text also has to overcome the issue of mis-recognizing similar looking alphabets. We propose a comprehensive solution to overcome this problem of recognizing words with phonetic spelling variations. To our knowledge, no work has been done till date to recognize Hinglish handwritten text. Our proposed solution shows a character recognition accuracy of 94% and word recognition accuracy of 72%, thus correctly recognizing the multiple phonetic variations of any given word.
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Mitra, S., Singh, V., Sahu, P.P., Veera, V., Venkatesan, S.M. (2018). Accommodating Phonetic Word Variations Through Generated Confusion Pairs for Hinglish Handwritten Text Recognition. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_11
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DOI: https://doi.org/10.1007/978-3-319-91947-8_11
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