Twenty Years of Document Image Analysis in PAMI
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical, Nonparametric Methodology for Document Degradation Model Validation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
An OCR System to Read Two Indian Language Scripts: Bangla and Devnagari (Hindi)
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Gujarati Character Recognition
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Recognition of Printed Amharic Documents
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Optical character recognition for printed Hindi text in Devnagari using soft-computing technique
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Engineering Applications of Artificial Intelligence
Nearest neighbor based collection OCR
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
A semi-automatic adaptive OCR for digital libraries
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Proceeding of the workshop on Document Analysis and Recognition
On performance analysis of end-to-end OCR systems of Indic scripts
Proceeding of the workshop on Document Analysis and Recognition
Applied Computational Intelligence and Soft Computing
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This paper describes the character recognition processfrom printed documents containing Hindi and Telugu text.Hindi and Telugu are among the most popular languages inIndia. The bilingual recognizer is based on Principal ComponentAnalysis followed by support vector classification.This attains an overall accuracy of approximately 96.7%.Extensive experimentation is carried out on an independenttest set of approximately 200000 characters. Applicationsbased on this OCR are sketched.