Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces | Zendy

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Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces

Author(s) -

R. Deepa,

Rajeswara Rao R

Publication year - 2019

Publication title -

international journal of innovative technology and exploring engineering

Language(s) - English

Resource type - Journals

ISSN - 2278-3075

DOI - 10.35940/ijitee.l3685.1081219

Subject(s) - tamil , character (mathematics) , scripting language , feature (linguistics) , artificial intelligence , computer science , pattern recognition (psychology) , variable (mathematics) , feature vector , font , natural language processing , speech recognition , mathematics , linguistics , mathematical analysis , philosophy , geometry , operating system

offline handwritten character recognition system has been a challenge for Indian scripts, especially for South Indian languages. Huge number of characters of local languages including alphabets, consonants and composite characters make the recognition system more complicated. A good recognition system for subset of Tamil script, a famous South Indian script, is proposed in this work. Variable length feature vector is extracted from the thinned character image. This extracted feature is given to a novel simple classification algorithm which works based on probability. A subset of Tamil script, 20 character classes, is considered for experiment. The samples were taken from HP Labs dataset for Tamil language and a recognition accuracy of 88.15% has been produced.

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