论文信息 - HMM-Based Online Handwriting Recognition System for Telugu Symbols

HMM-Based Online Handwriting Recognition System for Telugu Symbols

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 ACECADreg Digimemoreg and describe its usage in building a Telugu handwriting dataset.

L. Prasanth | V. J. Babu | R. Raghunath Sharma | A. Bharath

[1] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[2] C. Chandra Sekhar,et al. Online Handwritten Character Recognition of Devanagari and Telugu Characters using Support Vector Machines , 2006 .

[3] Alexander H. Waibel,et al. Online handwriting recognition: the NPen++ recognizer , 2001, International Journal on Document Analysis and Recognition.

[4] Alejandro Héctor Toselli,et al. Writing speed normalization for on-line handwritten text recognition , 2005, Eighth International Conference on Document Analysis and Recognition (ICDAR'05).