Bangla handwritten character recognition using convolutional neural network - Rahman, Md. Mahbubar; Akhand, Md. Aminul Haque; Islam, Shahidul; Shill, Pintu Chandra; Rahman, M.M. Hafizur (Modern Education and Computer Science (MECS) Press, 2015)
Handwritten character recognition complexity varies among different languages due to distinct shapes, strokes and number of characters. Numerous works in handwritten character recognition are available for English with respect to other major languages such as Bangla. Existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, Convolutional Neural Network (CNN) is found efficient for English handwritten character recognition. In this paper, a CNN based Bangla handwritten character recognition is investigated. The proposed method normalizes the written character images and then employ CNN to classify individual characters. It does not employ any feature extraction method like other related works. 20000 handwritten characters with different shapes and variations are used in this study. The proposed method is shown satisfactory recognition accuracy and outperformed some other prominent exiting methods.