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Automatic Text Recognition (ATR) - Step 1: Getting Started

This session outlines the entire workflow of humanities research projects utilising ATR to extract full text from scanned images. We provide an overview of each step in the process and introduce subsequent tutorials that delve deeper into these steps. Additionally, a ‘How to get started with ATR’ road map linked in the source will guide you through important questions and give you basic orientation before starting an ATR project.

You can read the blogpost (available in English, French, and German), and watch our video (with subtitles in English, French, and German) embedded in the post.

Learning Outcomes

After completing this resource, learners will be able to:

  • Identify the components of the ATR workflow relevant to humanities research.
  • Understand the basic principles and applications of Automatic Text Recognition.
  • Prepare their journey through the ATR pipeline by using our roadmap “How to get started in ATR?”
  • Prepare to integrate ATR technology into your research activities effectively.
Interested in learning more?

Check out "Automatic Text Recognition - Step 1: Getting Started"

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Cite as

Ariane Pinche and Pauline Spychala (2024). Automatic Text Recognition (ATR) - Step 1: Getting Started. Version 1.0.0. Edited by Anne Baillot and Mareike König. Deutsches Historisches Institut Paris. [Training module].

Reuse conditions

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

Full metadata

Automatic Text Recognition (ATR) - Step 1: Getting Started
Ariane Pinche, Pauline Spychala
Social Sciences and Humanities
Published to DARIAH-Campus:
Originally published:
Content type:
Training module
CCBY 4.0
Editing tools, Machine Learning, Automatic Text Recognition