Bangla Continuous Handwriting Character and Digit Recognition Using CNN

Abstract

There are several works in Bangla handwritten character recognition. Here a new methodology proposed to recognize the character from continuous Bangla handwritten character. The system’s main components are preprocessing, feature extraction, and recognition. There is a strong possibility that is found in Bangla words, and characters are overlapped. This problem often happens in handwritten texts like a consecutive character appears on another character. When it comes to Bangla characters, segmentation becomes much more difficult. To build an effective OCR system of Bangla handwritten text, recognition of characters is important as much as segmentation of characters. Here the main purpose is creating a system, which takes continuous Bangla handwritten text images as an input and then segments the input texts into its constituent words and finally segments each word into individual characters. In this present study, here we used EkushNet dataset model which includes 50 basic characters, 10 character modifiers, 52 frequently used conjunct characters, and 10 digits. By using our algorithm, we are able to segment 95% words from text and 90% characters from the words. Overall, in this present OCR system here recognition and segmentation of characters from handwritten Bangla texts are effectively dealing with the probable problems.