Post-OCR 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. Post-processing approaches detect and correct remaining errors to improve the quality of OCR texts. However, state-of-the-art approaches still need to be improved. Most of the existing post-OCR 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 character-level (including character noisy channel, index of peculiarity) to word-level (such as frequencies of n-grams, skip-grams, part-of-speech) Experimental results show that our approach outperforms the best performing techniques in the ICDAR 2017 Competition on Post-OCR text correction.