README.md

metadata

title: Chassis OCR
emoji: 🔧
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

Chassis OCR Pipeline

Reads engraved chassis numbers from phone images and matches them against barcode-scanned numbers.

Setup

pip install -r requirements.txt

On Linux you also need:

sudo apt-get install libzbar0

Project Structure

chassis_ocr/
├── images/
│   ├── barcode/     ← put your 50 barcode images here
│   └── chassis/     ← put your 50 chassis images here (same filenames)
├── results/         ← comparison images + report saved here
├── preprocess.py    ← image cleaning pipeline
├── barcode_scanner.py
├── ocr.py
└── evaluate.py      ← run this

Important — Naming Convention

Barcode and chassis images must have the same filename to be paired:

images/barcode/001.jpg  ←→  images/chassis/001.jpg
images/barcode/002.jpg  ←→  images/chassis/002.jpg

Run

# Test preprocessing on a single chassis image
python preprocess.py images/chassis/001.jpg

# Test OCR on a single chassis image  
python ocr.py images/chassis/001.jpg

# Run full evaluation on all 50 pairs
python evaluate.py

Output

After running evaluate.py:

• results/report.json — full accuracy breakdown
• results/*_comparison.jpg — before/after preprocessing for each image

Pipeline

Barcode image → pyzbar → ground truth string
                                              ↘
Chassis image → CLAHE → Adaptive threshold     Compare → ✅ Match / ❌ Mismatch
             → Morphological ops → PaddleOCR ↗