README.md

---
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

```bash
pip install -r requirements.txt
```

On Linux you also need:
```bash
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

```bash
# 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 ↗
```