Handwritten Bengali character recognition through geometry based feature extraction

© 2005 – ongoing JATIT & LLS Unlike English characters, one of the major drawbacks in recognizing handwritten Bengali script is the massive amount of characters in Bengali language and their complex shapes. There are 50 complex shaped characters in Bengali alphabet set and working with this huge amount of characters with an appropriate set of feature is a tough problem to solve. Moreover, the ambiguity and precision error are common in handwritten words. In addition, among the huge amount of complex shaped characters, some are very similar in shape those possess severe difficulty to recognize handwritten Bengali characters. Bearing in mind the complexity of the problem, an efficient approach for recognizing handwritten Bengali alphabet is proposed in this work. This proposed approach for identifying Bengali characters is based on character geometry-oriented feature extraction for different handwritten characters. In this paper, different image processing steps are used including image acquisition, digitization, preprocessing, segmentation and feature extraction for tackling the difficulty. Most importantly, the geometry based feature extraction method has been employed to extract the effective features from the Bengali characters for the classification purposes. Then, the classification result was measured for SVM and Artificial Neural Network (ANN) based classifiers on self-generated training and testing data sets which contain 2500 different samples of 50 characters in the Bengali character-set. The proposed technique produces an average recognition rate of 84.56% using SVM and 74.47% using ANN.