CMViT OCR: printed Indian language recognition using CMViT

In this research, we propose to recognize scanned line images of printed text in 11 Indian languages using CMViT, a single visual model created by merging ConvMixer with modified attention in Vision Transformer (SVTR). Recognizing the Indian language is challenging due to many reasons like cursive, making new shapes after joining in case of syllables, etc. There is also a similarity in some characters in Indian language scripts. Optical Character Recognition (OCR) is the science of recognizing and converting printed line images into analyzable, editable, and searchable forms. OCR has many use cases related to text extraction from scanned documents, handwritten documents, and scene text images. The research has been going on for the last decade by using artificial intelligence/machine learning tools to automatically analyze printed documents by converting them to editable digital text. Initially, AI-based OCR development started from RNN, LSTM, BiLSTM, and encoder-decoder architecture. The OCR is developed for 11 Indian languages named Bangla, Gujarati, Gurumukhi, Hindi, Kannada, Malayalam, Marathi, Oriya, Tamil, Telugu, Urdu, and 2 bilinguals named Hindi-English, and Marathi-English. The OCR CER for each language is in the range of 0.15% to 1.95% using the proposed network.