Back in 2021 we published a line-segmentation method for handwritten text recognition at IEEE TUAC, benchmarked on the IAM dataset. Revisiting it now, with a few more years of transformers and production ML under the belt, a few things stand out.
What held up
The core idea — segmenting before recognizing, rather than asking one end-to-end model to do both — still holds up surprisingly well for messy, real-world handwriting. Clean separation of concerns made the failure modes easier to debug, which mattered more in practice than squeezing out another point of benchmark accuracy.
What I’d redo
I’d spend less time hand-tuning the segmentation heuristics and more time on the training data itself. Most of the model’s mistakes traced back to underrepresented handwriting styles in IAM, not architecture choices — a lesson that keeps re-appearing in every ML project since, agentic pipelines included.
Segmentation is still the hard part. Recognizing a well-isolated line of text is close to solved; deciding where one line ends and the next begins in genuinely messy handwriting is where the interesting problems still live.