Multi-Script Line identification from Indian Documents
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Online Handwritten Script Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction and classification for bilingual script (Gurmukhi and Roman)
ACST'07 Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology
Word level multi-script identification
Pattern Recognition Letters
Local features-based script recognition from printed bilingual document images
International Journal of Computer Applications in Technology
Word level identification of Kannada, Hindi and English scripts from a tri-lingual document
International Journal of Computational Vision and Robotics
Script based text identification: a multi-level architecture
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
OCR of printed telugu text with high recognition accuracies
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Bangla/English script identification based on analysis of connected component profiles
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
An empirical intrinsic mode based characterization of Indian scripts
Proceeding of the workshop on Document Analysis and Recognition
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In a multi-lingual country like India, a document page may contain more than one script form. Under the three-language formula, the document may be printed in English, Devnagari and one of the other official Indian languages. For OCR of such a document page, it is necessary to separate these three script forms before feeding them to the OCRs of individual scripts. In this paper, an automatic technique of separating the text lines using script characteristics and shape based features is presented. At present, the system has an overall accuracy of about 98.5%.