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Article Dans Une Revue ACM Computing Surveys Année : 2021

Survey of Post-OCR Processing Approaches

Résumé

Optical character recognition (OCR) is one of the most popular techniques used for converting printed documents into machine-readable ones. While OCR engines can do well with modern text, their performance is unfortunately significantly reduced on historical materials. Additionally, many texts have already been processed by various out-of-date digitisation techniques.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 effects 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 outlines some research directions of this field.

Dates et versions

hal-03320329 , version 1 (15-08-2021)

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Citer

Thi Tuyet Hai Nguyen, Adam Jatowt, Mickael Coustaty, Antoine Doucet. Survey of Post-OCR Processing Approaches. ACM Computing Surveys, 2021, 54 (6), pp.1-37. ⟨10.1145/3453476⟩. ⟨hal-03320329⟩

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