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Recognition of text on word or line images, without the need for sub-wordsegmentation has become the mainstream of research and development of textrecognition for Indian languages. Modelling unsegmented sequences usingConnectionist Temporal Classification (CTC) is the most commonly used approachfor segmentation-free OCR. In this work we present a comprehensive empiricalstudy of various neural network models that uses CTC for transcribing step-wisepredictions in the neural network output to a Unicode sequence. The study isconducted for 13 Indian languages, using an internal dataset that has around1000 pages per language. We study the choice of line vs word as the recognitionunit, and use of synthetic data to train the models. We compare our models withpopular publicly available OCR tools for end-to-end document image recognition.Our end-to-end pipeline that employ our recognition models and existing textsegmentation tools outperform these public OCR tools for 8 out of the 13languages. We also introduce a new public dataset called Mozhi for word andline recognition in Indian language. The dataset contains more than 1.2 millionannotated word images (120 thousand text lines) across 13 Indian languages. Ourcode, trained models and the Mozhi dataset will be made available athttp://cvit.iiit.ac.in/research/projects/cvit-projects/

An empirical study of CTC based models for OCR of Indian languages