a filter based post-ocr accuracy boost system
Our current research effort aims at building a filter based post-OCR accuracy boost system that will combine different post-OCR correction filters to improve the OCR accuracy better than each individual filter can. In this paper we focus on a Hidden Markov Model (HMM) based accuracy booster modeling OCR engine noise generation as a two-layer stochastic process. We employ a commercial spell-checker both as another error correction filter and as a base line for accuracy boost comparison. We demonstrate the versatility of our approach in experiments with documents in English and Arabic.
ocr hmm error correction statistical language models information retrieval machine translation