Handwriting OCR is a specialist service built from the ground up for handwritten input, and it shows in the one number that matters: on our benchmark page it returned a 0.9% Word Error Rate, a single short-word substitution across a hundred words. Every other tool we tested made at least eight times as many errors on the same image.
What separates it from the cloud document APIs is that it was not adapted from a printed-text engine. Cursive letterforms, connected script, faded ink, and the drifting baselines of real handwriting are the problem it was designed for, not an edge case bolted on. Reading order held on our prose sample where general document OCR scrambled it, and it handles historical hands that push the LLM vision models below 50% accuracy.
For developers it is the most complete option: a clean REST API is available on every plan (including the free trial), with transcription, table extraction, and custom extractors that turn handwritten forms into typed JSON. That last capability is genuinely rare, and it is the reason the tool shows up in structured-data workflows and not just transcription.
The honest limitation is cost on the wrong input. If your documents are printed rather than handwritten, the cloud APIs do the job at a fraction of the price and you do not need this. On handwriting, though, the accuracy gap pays for itself on the first page you would otherwise have to retype.