receipt-text-detection_kr-pan_resnet18

🎯 Quick Start

📊 Performance

• Precision: 95.35%
• Recall: 95.72%
• H-Mean: 95.37%

🏗️ Architecture

• Base: ResNet18 encoder
• Decoder: PAN decoder
• Head: DBNet with polygon output
• Framework: PyTorch Lightning

📝 Training Details

• Dataset: Private Korean receipt/invoice corpus
• Epochs: 18/20
• Steps: 1,957
• Optimizer: AdamW (lr=0.001)

⚠️ Important Notes

1. The model contains learned representations, not original data
2. No training data is included in this repository
3. The model should be used responsibly in compliance with applicable laws

• Not attempt to extract or reverse-engineer training data
• Respect intellectual property rights
• Consider privacy implications when applying to real receipts
• Implement appropriate data protection measures

🔧 Model Specs

• Input: Variable resolution images (recommended 640×480)
• Output: Text region masks and polygon coordinates
• Model Size: ~11.7 MB
• Inference: ~50-100ms (GPU), ~300-500ms (CPU)

📚 Full Documentation

• Detailed architecture information
• Training configuration
• Usage examples
• Limitations and ethical considerations
• Citation information

📖 Project Reference

• Original: AIBootcamp13/upstageailab-ocr-recsys-competition-ocr-2
• Continuation: Wchoi189/upstageailab-ocr-recsys-competition-ocr-2

📄 License