Publication
Handwritten Bangla Character Recognition Using Deep Convolutional Neural Network: Comprehensive Analysis on Three Complete Datasets
Author(s): M Mashrukh Zayed, S M Neyamul Kabir Utsha, Sajjad Waheed
Research keywords: Handwritten Bangla character, Deep convolutional neural network, Banglalekha, Ekush, CMATERdb, Three full datasets, 28Ă—28 images, Bangla compound characters
Bangla handwritten character recognition is a difficult job compared to other languages due to the morphological complexity of adjacent characters and a wide variety of curvatures in writing styles people have. Another reason for that is the unique presence of compound characters. Most of the recent research works conducted in this field standardize Deep Convolutional Neural Network (DCNN) models for delivering the most effective outcomes. This paper proposes a DCNN model to classify all the character classes from three popular databases known as BanglaLekha Isolated, Ekush, and CMATERdb. As for BanglaLekha Isolated, our model achieves 93.446% accuracy on the 50 alphabets category and an overall 91.45% considering the whole dataset. The other two datasets, Ekush and CMATERdb result in 95.05% and 94.17% respectively, where the second one holds 171 classes of compound characters alone and performs 93.259% correctness, which is so far the best for this specific category in this dataset.
The aim of the research was to improve the basic structure of OCR based identification of documents and exam papers for the educational institutes with Bangla medium. The future modification of this research is to focus on detecting sequence of characters or words or sentences in Bangla Handwritten documents and implement it as a OCR based identification system.
