Robust recognition of documents by fusing results of word clusters

The word error rate of any optical character recognition system (OCR) is usually substantially below its component or character error rate. This is especially true of Indic lan-guages in which a word consists of many components. Cur-rent OCRs recognize each character or word separately and do not take advantage of document level constraints. We propose a document level OCR which incorporates infor-mation from the entire document to reduce word error rates. Word images are first clustered using a locality sensitive hashing technique. Individual words are then recognized using a (regular) OCR. The OCR outputs of word images in a cluster are then corrected probabilistically by comparing with the OCR outputs of other members of the same cluster. The approach may be applied to improve the accuracy of any OCR run on documents in any language. In particu-lar, we demonstrate it for Telugu, where the use of language models for post-processing is not promising. We show a rel-ative improvement of 28 % for long words and 12 % for all words which appear at least twice in the corpus. 1