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Automatic Text Recognition (ATR) - Video 5: Text Recognition and Post-ATR Correction

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Learning Outcomes

  • Build and train models to recognise and interpret text within images.
  • Apply concepts of machine learning specific to ATR to improve recognition accuracy.
  • Create ground truth data for training and validating ATR models.
  • Evaluate the effectiveness of different ATR models based on their output quality.

Cite as

Floriane Chiffoleau and Sarah Ondraszek (2024). Automatic Text Recognition (ATR) - Video 5: Text Recognition and Post-ATR Correction. Version 1.0.0. Edited by Anne Baillot and Mareike König. Deutsches Historisches Institut Paris. [Video]. https://youtu.be/P5O8bPR9hXg?si=MwmAmdLVdcQlpb5r

Reuse conditions

Resources hosted on DARIAH-Campus are subjects to the DARIAH-Campus Training Materials Reuse Charter

Full metadata

Title:
Automatic Text Recognition (ATR) - Video 5: Text Recognition and Post-ATR Correction
Authors:
Floriane Chiffoleau, Sarah Ondraszek
Domain:
Social Sciences and Humanities
Language:
en
Published:
5/10/2024
Content type:
Video
Licence:
CCBY 4.0
Sources:
DARIAH
Topics:
eHeritage
Version:
1.0.0