What You Will Learn
By the end of this introduction and the subsequent pages, you will confidently be able to:
- Define OCR, HTR, and ATR, and trace their evolution from punch-cards to neural networks
- Explain why ATR transforms archival research—saving time and revealing hidden patterns
- Map the modern ATR workflow on a conceptual level: from scanning to model training, transcription, and correction
- Decide when to use off-the-shelf models versus custom-trained ones for your specific collection
Why You Are Here
Historical documents—be they 12th-century charters, 16th-century letters, or 19th-century diaries—hold treasures of insight. Yet their handwritten scripts, aging inks, and paper wear make them difficult to read and analyze at scale. Automated Text Recognition (ATR) unlocks these texts, turning images of pages into machine-readable transcripts so you can:
⚙️ Boost Efficiency: Replace manual transcription with automated pipelines
🔍 Accelerate Discovery: Search for people, places, and phrases across collections
📊 Expand Analysis: Combine close reading of individual pages with corpus-wide statistics
A Quick Story: At the University of Zürich, ATR has turned what once would have been decades of manual transcription into working packages that wrap up within months. For instance, the Bullinger Digital team used a Transformer-based TrOCR model to automatically transcribe almost 3,000 Reformation-era letters. The PARES project will employ ATR to digitize the archives of French philologist Gaston Paris (1839–1903), and the Heinrich Wölfflin Gesammelte Werke initiative is applying automated transcription to the collected works of Swiss art historian Heinrich Wölfflin (1864–1945). Thanks to ATR, these landmark projects can devote their time to interpretation, text mining, and deeper epistemological questions—rather than the painstaking work of character-by-character transcription.
Who This Module Is For
No matter your background—historian, archivist, librarian, digital humanist, or curious learner—you will find this module approachable. We assume no prior AI, programming, or deep-learning knowledge. Familiarity with reading and interpreting historical scripts is helpful, but not required
Roadmap at a Glance
ATR Overview: Why it matters now and what makes it different from OCR/HTR
Benefits for Historical Research: Real-world case studies and quick wins
History in Brief: From early OCR to today’s transformer-based models
Under the Hood: Key components of an ATR pipeline (optional deep dive)
Resources: Definitions, further reading, and useful links