Archivists Turn to LLMs to Decipher Handwriting at Scale
When I sat down with bell hooks’ personal journals at an archive at Berea College in Kentucky, I expected an intimate peek into her private thoughts, her voice before the editing. What I got instead was frustration. Her handwriting was dense cursive, all loops that looked identical to my eye, and there were years of journals to go through. I found myself photographing pages and feeding them to ChatGPT just to read what she’d written. My tool of choice worked well, and it turn
Archivists are increasingly leveraging large language models (LLMs) to transcribe handwritten historical documents, a task that has long challenged artificial intelligence. Previously, deciphering dense or varied cursive handwriting required specialized paleography skills, custom software, or extensive manual effort. Recent advancements in general-purpose AI models, however, are proving surprisingly effective at reading these complex texts with remarkable speed and accuracy. Researchers have found that LLMs can outperform even specialized handwriting recognition software on historical documents, making vast collections of previously inaccessible materials searchable.
This development has the potential to unlock significant historical archives, enabling researchers and the public to access and analyze information that was previously too difficult or time-consuming to decipher.
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