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Survey of Post-OCR Processing Approaches

Authors:

Thi Tuyet Hai Nguyen,

Mickael Coustaty,

Antoine DoucetAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 6

Article No.: 124, Pages 1 - 37

https://doi.org/10.1145/3453476

Published: 13 July 2021 Publication History

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Abstract

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 Additionally, many texts have already been processed by various out-of-date digitisation techniques.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.

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Index Terms

Survey of Post-OCR Processing Approaches
1. Applied computing
  1. Document management and text processing
    1. Document capture
      1. Document analysis
    2. Document management
      1. Text editing
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 6

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July 2022

799 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3475936

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Published: 13 July 2021

Accepted: 01 February 2021

Revised: 01 January 2021

Received: 01 August 2020

Published in CSUR Volume 54, Issue 6

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