A student building a form-digitisation tool for a local school found that OCR that worked well on printed textbooks fell apart on handwritten admission forms. Handwriting recognition is a genuinely different problem, not a harder version of the same one.
Why it's harder
- Handwriting varies wildly between writers; printed text is drawn from a small set of consistent fonts.
- Forms in India often mix Devanagari and Roman scripts on the same line — a single-script model misses half the content.
- Scanned or photographed forms add skew, shadows and creases that clean printed-text datasets never include.
A practical approach
Use a vision-capable LLM for handwritten extraction rather than a classic OCR engine trained on printed fonts — modern multimodal models handle messier handwriting meaningfully better. Always keep a human-review step for any field that feeds something consequential, like marks or ID numbers.
See designing an OCR pipeline for PDFs for the printed-text baseline this builds on.
— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.