Geometrical Features Extraction and KNN Based Classification of Handwritten Marathi Characters
Parshuram M. Kamble, Ravindra S. Hegadi · 2017
Handwritten character recognition of the Marathi language is a challenging task because characters are complex in structure. 31320 samples of characters from different writers have been collected and database is prepared. Noise is removed by using morphological and thresholding operation. Skewed scanned pages and segmented characters are corrected using Hough Transformation. The characters are segmented from scanned pages by using bounding box techniques. Size variation of each handwritten Marathi characters are normalized in 40 X 40 pixel size. Here we propose feature extraction from handwritten Marathi characters using connected pixel based features like area, perimeter, eccentricity, orientation and Euler number. The k-nearest neighbor (KNN) algorithm with five fold validation has been used for result preparation. The accuracy of proposed method is 85.88 % obtained.