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HMM-based Online Handwriting Recognition System for Telugu Symbols

V, Jagadeesh Babu; L, Prasanth; R, Raghunath Sharma; G.V., Prabhakara Rao; A, Bharath


Keyword(s): hidden Markov models; online handwriting recognition; Telugu symbol recognition

Abstract: In this paper we present an online handwritten symbol recognition system for Telugu, a widely spoken language in India. The system is based on Hidden Markov Models (HMM) and uses a combination of time- domain and frequency-domain features. The system gives top-1 accuracy of 91.6% and top-5 accuracy of 98.7% on a dataset containing 29,158 train samples and 9,235 test samples. We also introduce a cost-effective and natural data collection procedure based on ACECAD® Digimemo® and describe its usage in building a Telugu handwriting dataset. Publication Info: ICDAR'2007, Curitiba, Brazil, 23-26 Sept., 2007

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