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 turns out I’m not the first person in an archive to have figured this out. Getting computers to reliably read human handwriting, in all its variations, has challenged AI researchers since the earliest days of the field.

Researchers in the 1960s predicted machines would soon simply devour handwritten text; instead the problem spawned decades of specialized research and entire commercial industries. Yann LeCun, who later went on to win the Turing Award for his contributions to deep learning, published landmark work on handwritten digit recognition in the 1980s that showed what was possible in narrow, controlled settings. Real archives were another matter. Now that boundary is moving. General-purpose AI models are…