HMM-Based Online Handwriting Recognition System for Telugu Symbols

Authors:
V. Babu;L. Prasanth;R. Sharma;G. V. Rao;A. Bharath
Affiliations:
Sri Sathya Sai Institute of Higher Learning, India;Sri Sathya Sai Institute of Higher Learning, India;Sri Sathya Sai Institute of Higher Learning, India;Sri Sathya Sai Institute of Higher Learning, India;Hewlett-Packard Labs
Venue:
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Year:
2007

Citing 0
Cited 6

Tilt menu: using the 3D orientation information of pen devices to extend the selection capability of pen-based user interfaces

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Performance analysis of a proposed smoothing algorithm for isolated handwritten characters

International Journal of Artificial Intelligence and Soft Computing
HMM-based online handwritten gurmukhi character recognition

Machine Graphics & Vision International Journal
Attention-Feedback Based Robust Segmentation of Online Handwritten Isolated Tamil Words

ACM Transactions on Asian Language Information Processing (TALIP)
Assamese online handwritten digit recognition system using hidden Markov models

Proceeding of the workshop on Document Analysis and Recognition
A data acquisition and analysis system for palm leaf documents in Telugu

Proceeding of the workshop on Document Analysis and Recognition

Quantified Score

Hi-index	0.00

Visualization

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.