Classification of Handwritten Tamil Characters using Variable Length Puzzle Pieces
Ashlin Deepa R N1, Rajeswara Rao R2

1Ashlin Deepa R N, Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad, India.
2Rajeswara Rao R, Department of Computer Science and Engineering, JNTUK University College of Engineering, Vizianagaram Andhra Pradesh, India. Email: 

Manuscript received on September 19, 2019. | Revised Manuscript received on 29 September, 2019. | Manuscript published on October 10, 2019. | PP: 4797-4801 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36851081219/2019©BEIESP | DOI: 10.35940/ijitee.L3685.1081219
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Abstract: 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.
Keywords:  Offline; Handwritten Character; Recognition, Classification, feature Extraction
Scope of the Article: Classification