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Deep Learning for Historical Document Analysis and Recognition-A Survey.

Francesco Lombardi1, Simone Marinai1

  • 1Department of Information Engineering (DINFO), School of Engineering, Università degli Studi di Firenze, 50139 Florence, Italy.

Journal of Imaging
|August 30, 2021
PubMed
Summary
This summary is machine-generated.

Deep learning is revolutionizing historical document analysis. Recent research introduces novel tasks and applications, moving beyond traditional problem-solving for better recognition and understanding.

Keywords:
artificial neural networksdeep learningdocument image analysis and recognitionhistorical documents

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Area of Science:

  • Computer Science
  • Digital Humanities
  • Artificial Intelligence

Background:

  • Deep learning methods are increasingly applied across diverse research fields.
  • The analysis and recognition of historical documents represent a growing area of application for these advanced techniques.

Purpose of the Study:

  • To provide a comprehensive survey of deep learning applications in historical document analysis.
  • To define historical documents within the research context and identify key sub-tasks.
  • To explore input-output relationships, prevalent models, datasets, and future research directions.

Main Methods:

  • Systematic review of recent literature on deep learning for historical documents.
  • Categorization of research based on defined tasks and input-output relations.
  • Analysis of commonly used deep learning models and available datasets.

Main Results:

  • The field has advanced significantly, with novel tasks and applications emerging.
  • Deep learning models are being adapted for new challenges in historical document recognition.
  • A variety of datasets are available, supporting diverse research applications.

Conclusions:

  • Current research represents a substantial leap forward in historical document analysis.
  • Future trends suggest continued innovation in tasks and methodologies.
  • The integration of deep learning offers promising avenues for future research in the field.