Comparative Study of Handwritten Marathi Characters Recognition Based on KNN and SVM Classifier

Robust handwritten Marathi character recognition is essential to the proper function in document analysis field. Many researches in OCR have been dealing with the complex challenges of the high variation in character shape, structure and document noise. In proposed system, 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 \(\times \) 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 modified k-nearest neighbor (KNN) and SVM algorithm with five fold validation has been used for result preparation. The comparative accuracy of proposed methods are recorded. In this experiment modified SVM obtained high accuracy as compared with KNN classifier.