Improving OCR for an Under-Resourced Script Using Unsupervised Word-Spotting

Optical character recognition (OCR) quality, especially for under-resourced scripts like Bangia, as well as for documents printed in old typefaces, is a major concern. An efficient and effective pipeline for OCR betterment is proposed here. The method is unsupervised. It employs a baseline OCR engine as a black box plus a dataset of unlabeled document images. That engine is applied to the images, followed by a visual encoding designed to support efficient word spotting. Given a new document to be analyzed, the black-box recognition engine is first applied. Then, for each result, word spotting is carried out within the dataset. The unreliable OCR outputs of the retrieved word spotting results are then considered. The word that is the centroid of the set of OCR words, measured by edit distance, is deemed a candidate reading.