What it means
HTR is technology that converts images of handwritten documents into machine-readable text. It typically uses AI models trained on examples of particular handwriting styles, scripts and periods.
Real-world example
A state archive holds parish registers and administrative files written in Kurrent, an old German script that few people can read today. Archivists transcribe a set of sample pages, use them to train an HTR model, and then apply it to thousands of pages, checking samples to estimate accuracy before publishing the searchable texts with a quality note.
Why it matters for AI adoption
HTR can open up historical and administrative records that were previously almost inaccessible. Accuracy varies widely with handwriting, script, language and document condition, so outputs should be labelled as machine-generated and assessed before being relied on.
Often confused with
Optical Character Recognition (OCR)
OCR handles printed text; HTR handles handwriting. HTR is generally harder and more dependent on training data that matches the documents.
Questions to ask
- How accurate is the model on our specific handwriting and scripts, and how was that measured?
- How will we show users that transcriptions are machine-generated and may contain errors?