Use of MKL as symbol classifier for Gujarati character recognition
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
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Indic Script Recognition is a difficult task due to the large number of symbols that result from concatenation of vowel modifiers to basic consonants and the conjunction of consonants with modifiers etc. Recognition of Gujarati script is a less studied area and no attempt is made so far to constitute confusion sets of Gujarati glyphs. In this paper, we present confusion sets of glyphs in printed Gujarati. Feature vector made up of Daubechies D4 Wavelet coefficients were subjected to two different classifiers, giving more than 96% accuracy for a larger set of symbols. Novel application of GR Neural-Net Architecture allows for fast building of a classifier for the large character data set. The combined approach of wavelet feature extraction and GRNN classification has given the highest recognition accuracy reported on this script.