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Title: Sub-stroke-wise relative feature for online indic handwriting recognition
Authors: Bhattacharya N.
Pratim Roy, Partha
Pal U.
Published in: ACM Transactions on Asian and Low-Resource Language Information Processing
Abstract: The main problem of Bangla (Bengali) and Devanagari handwriting recognition is the shape similarity of characters. There are only a few pieces of work on writer-independent cursive online Indian text recognition, and the shape similarity problem needs more attention from the researchers. To handle the shape similarity problem of cursive characters of Bangla and Devanagari scripts, in this article, we propose a new category of features called 'sub-stroke-wise relative feature' (SRF) which are based on relative information of the constituent parts of the handwritten strokes. Relative information among some of the parts within a character can be a distinctive feature as it scales up small dissimilarities and enhances discrimination among similar-looking shapes. Also, contextual anticipatory phenomena are automatically modeled by this type of feature, as it takes into account the influence of previous and forthcoming strokes. We have tested popular state-of-the-art feature sets as well as proposed SRF using various (up to 20,000-word) lexicons and noticed that SRF significantly outperforms the state-of-the-art feature sets for online Bangla and Devanagari cursive word recognition. © 2018 Association for Computing Machinery.
Citation: ACM Transactions on Asian and Low-Resource Language Information Processing (2018), 18(2): -
Issue Date: 2018
Publisher: Association for Computing Machinery
Keywords: Cursive text recognition
Indic script
Lexicon driven recognition
Online handwriting recognition
ISSN: 23754699
Author Scopus IDs: 55604675700
Author Affiliations: Bhattacharya, N., Bose Institute, Kolkata, India
Roy, P.P., Indian Institute of Technology Roorkee, India
Pal, U., Indian Statistical Institute, Kolkata, India
Appears in Collections:Journal Publications [CS]

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