| --- |
| title: KYC OCR (India) |
| emoji: 📄 |
| colorFrom: blue |
| colorTo: green |
| sdk: gradio |
| sdk_version: "5.0.0" |
| python_version: "3.12" |
| 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. |
|
|
