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Summary
This summary is machine-generated.

Deep learning (DL) approaches show promise for automatically analyzing ancient manuscripts in palaeography. This study compared DL with classical machine learning on the Avila Bible, finding DL effective for scribe identification.

Keywords:
classification systemsdeep learningfeature extractionpaleographywriter identification

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Area of Science:

  • Digital Humanities
  • Computer Vision
  • Palaeography

Background:

  • Advancements in digital imaging and algorithms enhance palaeography tools.
  • Feature selection is critical but challenging due to document variability.
  • Deep learning (DL) offers automatic feature extraction for ancient document analysis.

Purpose of the Study:

  • To evaluate DL as a general methodology for palaeography applications.
  • To compare DL performance against classical machine learning.
  • To test approaches on a challenging dataset of the 12th-century Avila Bible.

Main Methods:

  • Comparative analysis of DL and classical machine learning.
  • Application to scribe identification in manuscript images.
  • Utilizing a large dataset from the Avila Bible.

Main Results:

  • DL approaches demonstrated effectiveness in scribe identification.
  • Comparison highlighted DL's ability to generalize without prior knowledge.
  • The Avila Bible dataset served as a robust test case.

Conclusions:

  • Deep learning presents a viable general methodology for palaeography.
  • DL systems can automate feature extraction, simplifying analysis.
  • Further research can refine DL applications for historical document studies.