International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) 2007
DOI: 10.1109/iccima.2007.230
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Cited by 20 publications
(3 citation statements)
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“…Dholakia et al applied Daubechies D4 wavelet coefficients to extract the features from printed Gujarati characters. These feature vectors were used with the general regression neural network (GRNN) classifier and obtained 96% accuracy [24]. The structural feature extraction approach was introduced to classify printed Gujarati characters by Goswami and Mitra [25].…”
Section: A Traditional Machine Learning Approachesmentioning
confidence: 99%
“…Dholakia et al applied Daubechies D4 wavelet coefficients to extract the features from printed Gujarati characters. These feature vectors were used with the general regression neural network (GRNN) classifier and obtained 96% accuracy [24]. The structural feature extraction approach was introduced to classify printed Gujarati characters by Goswami and Mitra [25].…”
Section: A Traditional Machine Learning Approachesmentioning
confidence: 99%
“…In the paper [3] they worked on confusion set of glyphs. The combined approach of wavelet feature extraction and GRNN classification has given the highest recognition accuracy reported on this script as compare to nearest neighbor.…”
Section: Classificationmentioning
confidence: 99%
“…The strategy gives finite number of symbol classes therefore resulting less misclassification. In [5], the authors used Daubechies D4 wavelet transform based feature representation. For a subset of middle zone symbols belonging to 119 classes and 4173 examples(dependent vowel modifiers were not considered), the authors reported recognition accuracy of 97.59% with Neural Network based classifier.…”
Section: Introductionmentioning
confidence: 99%