A receipt scanner is a genuinely good student project: it's visual, it has a clear success metric, and it teaches the exact pattern used in real document-AI products.
The pipeline
- Photograph or upload the receipt image.
- Send it to a vision-capable model with a prompt asking for a strict JSON schema: merchant, date, line items, total.
- Validate the response against the schema and re-prompt once if it fails to parse.
- Cross-check: do the line items sum to the stated total, within a small tolerance for rounding?
The check that matters most
Step 4 is the whole project's credibility. A model can extract plausible-looking numbers that don't actually add up; catching that automatically, and flagging the receipt for manual review when they don't, is what separates a toy demo from something you'd trust with real expense data.
See document AI for invoices: an extraction schema for the schema-design half of this problem.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
