A Decision Support System For Handwritten Numerals Using Majority Voting
نویسندگان
چکیده
Feature extraction is an important step of pattern recognition. This study uses profile based feature extraction and uses Simple Profile (with cropping and without cropping image samples), Contour based feature extraction for recognizing handwritten numerals. The features are computed by using 48 X 48 as a feature length classifier used Back propagation neural network for the classification.. The classifier were trained and tested by using the CPAR Handwritten Numeral database. The average recognition rate of proposed system is observed as 87.10 % .
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