Recognition of unconstrained online Devanagari characters

Devanagari is a script used for several major languages such as Hindi, Sanskrit, Marathi and Nepali, and is used by more than 500 million people. Unconstrained Devanagari writing is more complex than English cursive due to the possible variations in the order, number, directional and shape of the constituent strokes. An online pen computing environment has numerous application in providing an easy human interface for a complex script like Devanagari. A Devanagari character recognition experiment with 20 different writers with each writer writing 5 samples of each character in a totally unconstrained way, has been conducted. An accuracy of 86.5% with no rejects is achieved through the combination of multiple classifiers that focus on either local online properties, or global off-line properties. Further improvements in performance are expected by using word-level contextual information. We also explore the use of writer dependent models to improve the recognition accuracy.

[1]  Anil K. Jain,et al.  Writer adaptation of online handwriting models , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[2]  R.M.K. Sinha,et al.  On Devanagari document processing , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[3]  Veena Bansal,et al.  On how to describe shapes of Devanagari characters and use them for recognition , 1999, Proceedings of the Fifth International Conference on Document Analysis and Recognition. ICDAR '99 (Cat. No.PR00318).

[4]  Anil K. Jain,et al.  Learning prototypes for online handwritten digits , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[5]  Anil K. Jain,et al.  Learning Prototypes for On-Line Handwritten Digits , 1998 .

[6]  Bidyut Baran Chaudhuri,et al.  An OCR system to read two Indian language scripts: Bangla and Devnagari (Hindi) , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.