Determination of the Script and Language Content of Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Script Identification From Document Images Using Cluster-Based Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Texture Features and Their Use in Automatic Script Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Script and Language Identification from Document Images
DIA '97 Proceedings of the 1997 Workshop on Document Image Analysis
Script Line Separation from Indian Multi-Script Documents
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Language identification for printed text independent of segmentation
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
Multi-Script Line identification from Indian Documents
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Digital Image Processing Using MATLAB
Digital Image Processing Using MATLAB
Texture for Script Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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In a multi script environment, majority of the documents may contain text information printed in more than one script/language forms. For automatic processing of such documents through optical character recognition (OCR), it is necessary to identify different script regions of the document. With this context, this paper proposes to develop a model to identify and separate text words of Kannada, Hindi and English scripts from a printed tri-lingual document. The proposed method is trained to learn thoroughly the distinct features of each script. The binary tree classifier is used to classify the input text image. Experiments were conducted on manually created document images of size 600 × 600 pixels. The results are very encouraging and prove the efficacy of the proposed model. The average success rate is found to be 98.8% for manually created dataset and 98.5% for dataset constructed from scanned document images.