Bangla optical character recognition through segmentation using curvature distance and multilayer perceptron algorithm

This paper presents an optical character recognition approach especially for Bangla offline printed characters. Separation of lines, words and individual characters are the main difficulties in printed Bangla character recognition due to different shapes of characters. Different techniques have been applied and performance is examined. It has been studied that no particular algorithm is found for efficient feature extraction. Feature selection is an essential step of optical character recognition. Accurate and distinguable feature plays an important role to leverage the performance of a classifier. A novel feature extraction scheme based on “Discrete Frechet Distance” and “Dynamic Time wrapping” is proposed. Probability of occurrence of the pixels of the given character of different font are calculated which are used to train Multilayer perceptron Neural Network. Experimental results show 100% accuracy for trained characters and overall 90–95% accuracy for all basic characters of the developed method on training and the tests sets respectively.