Script Identification in Printed Bilingual Documents
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Language Identification of Character Images Using Machine Learning Techniques
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Word level multi-script identification
Pattern Recognition Letters
Curvature feature distribution based classification of Indian scripts from document images
Proceedings of the International Workshop on Multilingual OCR
Word level identification of Kannada, Hindi and English scripts from a tri-lingual document
International Journal of Computational Vision and Robotics
Contribution to the discrimination of the medieval manuscript texts: application in the palaeography
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Bangla/English script identification based on analysis of connected component profiles
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Script identification from indian documents
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
HVS inspired system for script identification in indian multi-script documents
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Proceeding of the workshop on Document Analysis and Recognition
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This paper presents efficient algorithms for determining the language classification of machine generated documents without requiring the identification of individual characters. Such algorithms may be useful for sorting and routing of facsimile documents as they arrive so that appropriate routing and secondary analysis, which may include OCR, is selected for each document. It may also prove useful as a component of a content addressable document access system. There have been numerous reported efforts which attempt to segment printed documents into homogeneous regions using Hough transforms, hidden Markov models, morphological filtering, and neural networks. However, language identification can be accomplished without explicit segmentation using the less computationally intensive methods described.