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README.md

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----
-title: KYC OCR (India)
-emoji: 📄
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: "4.44.0"
-app_file: app.py
-pinned: false
-license: mit
-short_description: Extract fields from Indian PAN cards and cheques on CPU
----
-
-# 🇮🇳 KYC Document OCR
-
-A lightweight, CPU-friendly pipeline for extracting structured fields from
-Indian KYC documents (PAN card, cheque) from images or PDFs.
-
-Built on [RapidOCR](https://github.com/RapidAI/RapidOCR) — ONNX-runtime
-deployment of PaddleOCR PP-OCRv4 models. No GPU needed.
-
-## What it does
-
-- **PDF or image input** (PyMuPDF rasterises PDFs at 300 DPI)
-- **Document type auto-detection** via keyword + regex scoring
-- **Cheque rotation correction** — detects sideways-scanned cheques (text
-  spans more vertically than horizontally) and rotates before MICR extraction
-- **Format-spec validation** — PAN regex with 4th-character entity-type
-  lookup, IFSC regex, date normalisation, MICR positional parse
-
-## Supported documents
-
-| Document | Fields extracted |
-|---|---|
-| **PAN card** | PAN number, name, father's name (individual) / company name, DOB / date of incorporation, entity type (Individual / Company / HUF / Firm / Trust / etc.) |
-| **Cheque** | Bank name, branch address, IFSC code, account number, 9-digit MICR code, cheque number, transaction code |
-| **Aadhaar** | _Coming soon — recommended path: read the secure QR code via `pyzbar` rather than OCR_ |
-| **Passport** | _Coming soon — recommended path: parse the MRZ via `passporteye`_ |
-
-## How to use the Space
-
-1. Drop a PDF or image into the upload box
-2. Click **Extract fields**
-3. View the structured output and raw OCR (for debugging)
-
-The included example is a synthetic PAN with fake data so you can try it
-without uploading real documents.
-
-## Running locally
-
-```bash
-git clone <this-space-url>
-cd <repo>
-pip install -r requirements.txt
-python app.py
-```
-
-Then open `http://127.0.0.1:7860`.
-
-## Using `kyc_extract.py` directly as a library
-
-```python
-from kyc_extract import process, RapidOCR
-
-engine = RapidOCR()
-results = process("path/to/document.pdf", engine)
-
-for page in results:
-    print(page["detected_type"])     # 'pan' or 'cheque'
-    print(page["fields"])             # structured dict
-```
-
-## Privacy
-
-This is a public Space. **Do not upload real personal documents.** For
-production KYC pipelines, fork this repo and deploy on your own private
-infrastructure (DPDP Act and UIDAI guidelines require it for Aadhaar in
-particular).
-
-## Architecture notes
-
-- All extraction is rule-based — no model is hardcoded to specific names,
-  banks, PANs, or addresses. The same script handles any PAN or any bank's
-  cheque.
-- Document classification uses keyword + regex scoring; if confidence is
-  low, type is `unknown` and no extractor runs.
-- For cheques in portrait-orientation PDFs (common when phones are held
-  vertically), the pipeline detects the layout anomaly and re-OCRs the
-  rotated image, merging text from both passes so address and IFSC don't
-  get lost in the rotation.
-
-## Limitations
-
-- English-only OCR — Hindi label text (`नाम/Name`) comes out garbled but
-  the pipeline has shape-based fallbacks so this doesn't break field
-  extraction. For full Hindi extraction, add `lang='hi'` to RapidOCR.
-- MICR fonts are E-13B and not perfectly read by general OCR. The pipeline
-  reconstructs the 9-digit MICR from OCR fragments via positional parsing,
-  which works on most cheques but degrades on poor scans.
-- Aadhaar and passport extractors are stubs. The proper approach uses QR
-  and MRZ respectively, not pure OCR.