A New Methodology for Gray-Scale Character Segmentation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
GREC'09 Proceedings of the 8th international conference on Graphics recognition: achievements, challenges, and evolution
Bangla/English script identification based on analysis of connected component profiles
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Word level script recognition for Uighur document mixed with English script
Proceedings of the 4th International Workshop on Multilingual OCR
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We propose a practical scheme for multilingual multi font, and multi size large set character recognition using self organizing neural network. In order to improve the performance of the proposed scheme, a nonlinear shape normalization based on dot density and three kinds of hierarchical features are introduced. For coarse classification, two kinds of classifiers are proposed. One is a hierarchical tree classifier, and the other is a SOFM/LVQ based classifier which is composed of an adaptive SOFM coarse classifier and LVQ4 language classifiers. For fine classification, an LVQ4 classifier has been adopted. In order to evaluate the performance of the proposed scheme, recognition experiments with 3,367,200 characters having 7320 different classes have been carried out on a 486 DX-2 66 MHz PC. Experimental results reveal that the proposed scheme using an adaptive SOFM coarse classifier, LVQ4 language classifiers, and LVQ4 fine classifiers has a high recognition rate of over 98.27% and a fast execution time of more than 40 characters per second.