System for Enhancing Accuracy of Noisy Text using Deep Network Language Models

Text from image documents must be recognized for its usage. Various tasks such as plagiarism & error check, language analysis, information capture rely on the accuracy of this text conversion. OCR systems convert the document images to their text equivalent. These OCR systems are prone to introducing errors during the recognition process.This work reports a system developed to ingest image documents which is converted to text using available OCR technologies. The recognized text, subsequently, is processed with deep network language models to enhance the accuracy of text. The system consists of a client server architecture with user interface available from web application as well as from mobile app. For the language models, encoder-decoder based BART & MarianMT are used. The results obtained demonstrate a 35% reduction in WER using the BART language model.