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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 ↗
```
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