A Novel Weighted SVM Classifier Based on SCA for Handwritten Marathi Character Recognition
Surendra P. Ramteke, Ajay Anil Gurjar, D. S. Deshmukh · IETE Journal of Research · 2019
The research on handwritten optical character recognition (OCR) of Marathi script is very challenging due to the complex structural properties of the script that are not observed in most other scripts. This paper gives an OCR framework for handwritten Marathi document classification and recognition system. Due to the large variety of symbols the Marathi characters recognition poses great challenge and their proximity in appearance. The weighted one-against-rest support vector machines (WOAR-SVM) assume a noteworthy part to deal with vast feature measures which are utilized for the classification. Here, a new sine cosine algorithm is proposed for the identification of handwritten Marathi text. By utilizing different morphological operations the preprocessing is finished and the Marathi text is flexibly segmented in three levels; line segmentation, word segmentation and character segmentation with Modified Pihu method. Various features like statistical, global transformation, geometrical and topological features are extracted from the preprocessed image by extraction techniques. Result obtained show that various features with WOAR-SVM classifier perform the best by yielding high accuracy as 95.14%.