README.md

metadata

title: Receipt Scanner
emoji: 🧾
colorFrom: yellow
colorTo: blue
sdk: gradio
app_file: app.py
pinned: false
license: mit

Receipt Scanner

Question

How do we turn a document image into structured data a program can use?

System Boundary

This Space treats receipt understanding as a multimodal extraction problem: image in, schema out.

Method

A vision-language model reads the uploaded receipt and produces structured fields such as merchant, date, item rows, subtotal, tax, total, and payment details. The app parses the model output into table and JSON views.

Technique

This is multimodal information extraction. The model must read pixels, infer document layout, identify fields, and emit a schema that downstream software can consume.

The difficult part is not only recognizing text. The difficult part is assigning text to the correct semantic field: item, price, tax, total, date, or merchant.

Output

The app returns a summary, an item table, raw structured JSON, and exportable records.

Why It Matters

The useful part of document AI is not OCR alone. The useful part is converting messy visual evidence into validated fields that can enter a database, review queue, or accounting workflow.

What To Notice

Check whether totals reconcile with item rows and whether the model preserves uncertainty. Structured extraction should be judged at the field level, not only by a nice-looking summary.

Effect In Practice

Receipt extraction is a small version of a larger document-understanding pattern used for invoices, insurance forms, procurement, and expense workflows.

Hugging Face Extension

The Space can be extended with a receipt dataset, field-level accuracy metrics, and model comparisons across open vision-language models.

Limitations

Receipt formats vary widely. A production system should add confidence estimates, field-level validation, human review, and evaluation on a labeled receipt dataset.

Run Locally

pip install -r requirements.txt
python app.py