2-D Shape Classification Using Hidden Markov Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
Statistical methods for speech recognition
Statistical methods for speech recognition
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov Models for Online Handwritten Tamil Word Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
A hybrid approach for multifont Arabic characters recognition
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
A new algorithm for Arabic optical character recognition
AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Neural-network classifiers for recognizing totally unconstrained handwritten numerals
IEEE Transactions on Neural Networks
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In spite of several advancements in technologies pertaining to Optical character recognition, handwriting continues to persist as means of documenting information for day-to-day life. The process of segmentation and recognition pose quiets a lot of challenges especially in recognizing cursive handwritten scripts of different languages. The concept proposed is a solution crafted to perform character recognition of hand-written scripts in Tamil, a language having official status in India, Sri Lanka, and Singapore. The approach utilizes discrete Hidden Markov Models (HMMs) for recognizing off-line cursive handwritten Tamil characters. The tolerance of the system is evident as it can overwhelm the complexities arise out of font variations and proves to be flexible and robust. Higher degree of accuracy in results has been obtained with the implementation of this approach on a comprehensive database and the precision of the results demonstrates its application on commercial usage. The methodology promises to present a simple and fast scaffold to construct a full OCR system extended with suitable pre-processing.