About the Project
Bridging the Gap Between Ink and Digital Text
For decades, the personal correspondence of Texas A&M’s earliest leaders remained locked behind the beautiful but difficult cursive of the 19th and early 20th centuries. While digitization has made these letters available as images, the "data" within them—the names, the struggles of a burgeoning university, and the nuances of campus life—remained largely unsearchable and inaccessible to modern screen readers.
Using AI to Read Historic Letters is an experimental digital exhibit that explores how Handwritten Text Recognition (HTR) can unlock these archives. By leveraging the Google Gemini multimodal model, we transform static images into dynamic, searchable, and interpreted text.
The Vision: Transparency in Transcription
Traditional AI HTR transcription often operated as a "black box"; a user provided an image, and the model provided text with little explanation of how it arrived at its conclusion. Occassioanally, the model may hallucinate leading to poor results and mistrust.
AI Reasoning, or using multiple models in a feedback loop, results in amazing results and according to an article by Dan Cohen, performs transcription the same way you are "taught to do in a paleography class."
By utilizing the IIIF Paleography utility, we ask the AI to perform two distinct tasks for every letter:
- Transcribe: Create a highly accurate, marked-up version of the text with minor structural markup.
- Rationalize: Provide a "commentary" annotation that explains why certain words were chosen, how the model handled difficult handwriting, and how it utilized historical context to fill in the blanks.
Our Methodology
This exhibit is powered by three core pillars:
- The Archive: IIIF manifests of original letters from the Texas A&M University AMC Early President collections.
- The Standard: The International Image Interoperability Framework (IIIF), which allows us to serve high-resolution images and attach annotations directly to each page.
- The Intelligence: Large Language Models (LLMs) acting as a "digital paleographer" to interpret the unique flourishes and abbreviations of historical script.
Why This Matters
This project isn't just about speed; it's about accuracy and trust. By revealing the reasoning process, we allow libarians, archivists, historians, students, and archivists to see where the AI excels and where it might struggle. This dialogue between human expertise and machine learning paves the way for a future where historical archives are more accessible, inclusive, and deeply understood.