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Deep Learning for Historical Document Analysis and Recognition—A Survey

Journal of imaging, 2020-10, Vol.6 (10), p.110 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2020 by the authors. 2020 ;ISSN: 2313-433X ;EISSN: 2313-433X ;DOI: 10.3390/jimaging6100110 ;PMID: 34460551

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  • Title:
    Deep Learning for Historical Document Analysis and Recognition—A Survey
  • Author: Lombardi, Francesco ; Marinai, Simone
  • Subjects: 4th century ; 5th century ; Algorithms ; artificial neural networks ; Deep learning ; Digitization ; document image analysis and recognition ; historical documents ; Libraries ; Machine learning ; Neural networks ; Office automation ; Recognition ; Trends
  • Is Part Of: Journal of imaging, 2020-10, Vol.6 (10), p.110
  • Description: Nowadays, deep learning methods are employed in a broad range of research fields. The analysis and recognition of historical documents, as we survey in this work, is not an exception. Our study analyzes the papers published in the last few years on this topic from different perspectives: we first provide a pragmatic definition of historical documents from the point of view of the research in the area, then we look at the various sub-tasks addressed in this research. Guided by these tasks, we go through the different input-output relations that are expected from the used deep learning approaches and therefore we accordingly describe the most used models. We also discuss research datasets published in the field and their applications. This analysis shows that the latest research is a leap forward since it is not the simple use of recently proposed algorithms to previous problems, but novel tasks and novel applications of state of the art methods are now considered. Rather than just providing a conclusive picture of the current research in the topic we lastly suggest some potential future trends that can represent a stimulus for innovative research directions.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2313-433X
    EISSN: 2313-433X
    DOI: 10.3390/jimaging6100110
    PMID: 34460551
  • Source: PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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