razzak-zoningfeaturesand-2016.pdf (640.42 kB)
Zoning features and 2DLSTM for urdu text-line recognition
conference contribution
posted on 2016-01-01, 00:00 authored by S Naz, S B Ahmed, R Ahmad, Imran RazzakImran Razzak© 2016 The Authors. Published by Elsevier B.V. Recognition of Urdu cursive script is a challenging task due to the implicit complexities associated with it. The performance of a recognition system is immensely dependent on extracted features. There are various features extraction approaches proposed in recent years. Among many, an approach based on zoning features proved to be efficient and popular. Such zoning features represent significant information with low complexity and high speed. In this paper, we used zoning features for the classification of Urdu Nasta'liq text lines, with a combination of 2-Dimensional Long Short Term Memory networks (2DLSTM) as learning classifier. The proposed model is evaluated on publicly available UPTI dataset and character recognition rate of 93.39% is obtained.
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Event
Knowledge Based and Intelligent Information and Engineering Systems (20th : 2016 : York, England)Publisher
ElsevierLocation
York, EnglandPlace of publication
Amsterdam, The NetherlandsPublisher DOI
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Start date
2016-09-05End date
2016-09-07eISSN
1877-0509Language
engNotes
Published in the journal Procedia Computer Science, vol 96, pp.16-22Publication classification
E1.1 Full written paper - refereedTitle of proceedings
KES2016 : 20th International Conference on Knowledge Based and Intelligent Information and Engineering SystemsUsage metrics
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