Survey of Post-OCR Processing Approaches
- 1. University of La Rochelle, L3i
- 2. University of Innsbruck
Description
Optical character recognition (OCR) is one of the most popular techniques used for converting printed documents into machine-readable ones. OCR engines can perform well on modern text, unfortunately, their performance is significantly reduced on historical materials. Additionally, many texts have been already processed by various out-of-date digitisation techniques. As a consequence, digitised texts are noisy and need to be post-corrected. This article clarifies the importance of enhancing quality of OCR results by studying their affects on information retrieval and natural language processing applications. We then define the post-OCR processing problem, illustrate its typical pipeline, and review the state-of-the-art post-OCR processing approaches. Evaluation metrics, accessible datasets, language resources, and useful toolkits are also reported. Furthermore, the work identifies the current trend and outline some research directions of this field.
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