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Optical Character Recognition, Handwritten Gurmukhi Script, Wavelet Transforms, Feature Extraction, Zonal Densities
This paper presents an OCR (optical character recognition) system for the handwritten Gurmukhi numerals. A lot of work has been done in recognition of characters and numerals of various languages like English, Chinese, and Arabic etc. But in case of handwritten Gurmukhi script very less work has been reported. Different Wavelet transforms are used in this work for feature extraction. Also zonal densities of different zones of an image have been used in the feature set. In this work, 100 samples of each numeral character have been used. The back propagation neural network has been used for classification. An average recognition accuracy of 88.83% has been achieved.
Pritpal Singh, Sumit Budhiraja, "Offline Handwritten Gurmukhi Numeral Recognition using Wavelet Transforms", International Journal of Modern Education and Computer Science(IJMECS), vol.4, no.8, pp.34-39, 2012. DOI:10.5815/ijmecs.2012.08.05
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