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
Segmentation of touching characters in upper zone in printed Gurmukhi script
Proceedings of the 2nd Bangalore Annual Compute Conference
CASE'09 Proceedings of the fifth annual IEEE international conference on Automation science and engineering
Multi-oriented Bangla and Devnagari text recognition
Pattern Recognition
Unconstrained Bangla online handwriting recognition based on MLP and SVM
Proceedings of the 2011 Joint Workshop on Multilingual OCR and Analytics for Noisy Unstructured Text Data
An improved contour-based thinning method for character images
Pattern Recognition Letters
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One of the important reasons for poor recognition rate in optical character recognition (OCR) system is the error in character segmentation. Existence of touching characters in the scanned documents is a major problem to design an effective character segmentation procedure. In this paper, a new technique is presented for identification and segmentation of touching characters. The technique is based on fuzzy multifactorial analysis. A predictive algorithm is developed for effectively selecting possible cut columns for segmenting the touching characters. The proposed method has been applied to printed documents in Devnagari and Bangla: the two most popular scripts of the Indian sub-continent. The results obtained from a test-set of considerable size show that a reasonable improvement in recognition rate can be achieved with a modest increase in computations.