A Lenet-5 Based Bangla Handwritten Digit Recognition Framework

Abstract

Hand composed Digit recognition in Bangla language is a valuable beginning stage for creating an Optical Character Recognition in the Bengali language. Be that as it may, absence of huge and honest data collection, recognition of Bangla digit was not build already. In any case, in this outline, a colossal & honest data source known as NumtaDB is utilized for recognition of Bengali digits. The troublesome endeavor is connected to getting the solid presentation and high precision for gigantic, fair, common, natural and particularly extended NumtaDB dataset. So various sorts of preprocessing frameworks are utilized for planning pictures and a significant convolutional neural network is utilized for the request of representation in this paper. The LeNet-5 architecture based convolutional neural network model has indicated superb execution. We have accomplished 97.5% testing exactness which is a decent outcome for huge and fair NumtaDB dataset contrasting with other one-sided datasets. A wide range of preprocessing of pictures is additionally significant before preparing. We utilize some preprocessing strategies for obscure and loud pictures yet these are insufficient for the elite. An examination of the system brings out the EMNIST and MNIST datasets was performed so as to sustain the appraisal.