Handwritten OCR for word in Indic Language using Deep Networks

A large number of Indian documents are handwritten and India is a diverse nation with many languages. These handwritten documents contain important historical and cultural information which needs to be preserved by converting to digital format. The major problem is everyone has unique handwriting with different styles of writing. To address this problem, we have trained Handwritten Optical Character Recognition (HOCR) in eight Indian languages i.e. Bangla, Gujarati, Gurumukhi, Hindi, Kannada, Odia, Telugu, and Urdu. The datasets IIIT-HW-Dev and IIIT-HW-Telugu refer to a Devanagari dataset and a Telugu dataset respectively. The IIITINDIC-HW-WORDS consists of 872K handwritten words written in 8 Indic scripts by 135 writers. Devanagari and Telugu datasets are comprised of 95K and 120K handwritten words respectively. Tamil and Malayalam languages are excluded due to issues in the IIIT-INDIC-HW-WORDS dataset. The paper describes how the CNN-Transformer architecture leverages visual and textual features to perform OCR tasks in different languages. The model takes word images as input then CNN generates visual features, and feeds them to the transformer decoder for text generation. An encoder ResNet18 and a decoder from a transformer have been used for all eight languages to evaluate the performance of this architecture. This architecture performed best in Kannada with just a 1.5% character error rate.