A Cost Efficient Approach to Correct OCR Errors in Large Document Collections Word error rate of an OCR is often higher than its character error rate. This is especially true when OCRs are designed by recognizing characters. High word accuracies are critical for many practical applications like content creation and texttospeech systems. In order to detect and correct the misrecognised words, it is common for an OCR to employ a postprocessor module to improve the word accuracy. However, conventional approaches to postprocessing like looking up a dictionary or using a statistical language model (SLM), are still limited. In many such scenarios, it is often required to remove the outstanding errors manually. We observe that the traditional postprocessing schemes look at error words sequentially, since OCRs process documents one at a time. We propose a costefficient model to address the error words in batches rather than correcting them individually. We exploit the fact that a collection of documents (eg. a book), unlike a single document, has a structure leading to repetition of words. Such words, if efficiently grouped together and corrected together, can lead to a significant reduction in the effort. Error correction can be fully automatic or with a human in the loop. We compare the performance of our method with various baseline approaches including the case where all the errors are removed by a human. We demonstrate the efficacy of our solution empirically by reporting more than 70% reduction in the human effort with near perfect error correction. We validate our method on books in both English and Hind

PostOCR Error Detection by Generating Plausible Candidates The accuracy of Optical Character Recognition (OCR) technologies considerably impacts the way digital documents are indexed, accessed and exploited. Postprocessing approaches detect and correct remaining errors to improve the quality of OCR texts. However, stateoftheart approaches still need to be improved. Most of the existing postOCR techniques use predefined error position lists or apply simple techniques to detect errors. In this paper, we describe a novel error detector using different features from characterlevel (including character noisy channel, index of peculiarity) to wordlevel (such as frequencies of ngrams, skipgrams, partofspeech) Experimental results show that our approach outperforms the best performing techniques in the ICDAR 2017 Competition on PostOCR text correction.