Towards Building A Robust Large-Scale Bangla Text Recognition Solution Using A Unique Multiple-Domain Character-Based Document Recognition Approach
AKM Shahariar Azad Rabby, Md. Majedul Islam, Zahidul Islam, Nazmul Hasan, Fuad Rahman · 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) · 2021
Bangla is one of the world’s top ten popular languages in terms of the number of speakers. It also happens to have a complex script primarily because of complex characters, e.g. graphemes, composed of multiple single characters, and the characteristic short-hands, e.g. vowel diacritics, and consonant diacritics making the number of classes of this script recognition quite large, varied, and challenging. In this paper, we present a unique large-scale Bangla document OCR solution based on character-level recognition modules. We have tested our approach on two independent domains: printed and handwritten documents. We also applied our solution to three subdomains within the printed domain: computer-composed documents, letterpress documents, and typewritten documents. Our extensive experiments show that our approach achieves state-of-the-art performance on handwritten and printed documents.