https://github.com/ollycassidy13/captcha
Python toolflow to pass CAPTCHA tests
https://github.com/ollycassidy13/captcha
captcha captcha-solver tensorflow
Last synced: 2 months ago
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Python toolflow to pass CAPTCHA tests
- Host: GitHub
- URL: https://github.com/ollycassidy13/captcha
- Owner: ollycassidy13
- License: mit
- Created: 2025-06-16T14:28:37.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2025-06-18T10:12:37.000Z (about 1 year ago)
- Last Synced: 2025-06-18T11:25:08.211Z (about 1 year ago)
- Topics: captcha, captcha-solver, tensorflow
- Language: Python
- Homepage:
- Size: 6.84 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
Awesome Lists containing this project
README
# 🔓 CAPTCHA Breaker
*Breaking CAPTCHAs with 98%+ accuracy using Convolutional Recurrent Neural Networks and CTC loss*
---
## 🌟 Headlines
- **🎯 98%+ character accuracy** on synthetic CAPTCHAs
- **⚡ GPU-accelerated training** with mixed precision
- **🔄 End-to-end pipeline** from data generation to deployment
- **🧠 Full CTC evaluation**
- **🎨 Synthetic data generation** - no manual labeling required
## 🚀 Quick Start
Get up and running in minutes:
### 1️⃣ Install Dependencies
```bash
pip install tensorflow tensorflow-addons omegaconf hydra-core tqdm matplotlib captcha pillow
```
### 2️⃣ Generate Training Data
```bash
python generate_synthetic_dataset.py -n 20000
```
*Creates 20,000 synthetic CAPTCHA images like `ABC123_001.png`*
### 3️⃣ Train the Model
```bash
python train.py
```
*Auto-detects GPU based on TensorFlow, trains with mixed precision, saves best model*
### 4️⃣ Test Your Model
```bash
python evaluate.py
python predict.py path/to/your/captcha.png
```
---
## 📖 How It Works
### 🧠 The Architecture
Our CRNN (Convolutional Recurrent Neural Network) combines three powerful components:
```
📸 CAPTCHA Image → 🔍 CNN Feature Extractor → 🔄 Bidirectional LSTM → 📝 CTC Decoder → ✨ Text Output
```
#### 1. **CNN Backbone** 🔍
- ResNet-inspired feature extractor
- Batch normalization + ReLU activation
- Progressive max pooling for spatial reduction
- Converts images to rich feature representations
#### 2. **Sequence Modeling** 🔄
- **Bidirectional LSTM layers** capture left-to-right AND right-to-left context
- Handles variable-length sequences automatically
- Dropout prevents overfitting
#### 3. **CTC Magic** ✨
- **Connectionist Temporal Classification** eliminates need for character-level alignment
- Handles variable-length outputs elegantly
- Proper decoding removes duplicates and blank tokens
### 🎯 Why CTC is Crucial
**❌ Traditional Approach:**
```
Requires: [A][B][C][1][2][3] ← Exact alignment needed
```
**✅ CTC Approach:**
```
Handles: [A][A][_][B][C][_][1][2][3][3][_] ← Automatic alignment
↓ CTC Decoding ↓
Output: ABC123
```
---
## 🎯 Results & Performance
### 📊 Accuracy Metrics
| Metric | Score | Description |
|--------|-------|-------------|
| **Character Accuracy** | 99.3% | Individual character recognition |
| **Sequence Accuracy** | 97.9% | Complete CAPTCHA solved correctly |
| **Training Time** | <1 hour | On RTX 3050 (mixed precision) |
| **Inference Speed** | ~10ms | Per image on GPU |
### 🏆 Benchmarks
**Training Performance:**
- **Dataset:** 20,000 synthetic CAPTCHAs
- **Convergence:** 20-40 epochs (early stopping)
- **Memory Usage:** ~2GB GPU memory
- **Speed:** 40% faster with mixed precision
### 📈 Learning Curves
The model typically shows:
- Rapid initial learning (epochs 1-10)
- Gradual improvement (epochs 10-30)
- Convergence with early stopping
---
### 🐛 Troubleshooting
**Common Issues:**
1. **CUDA out of memory:**
```yaml
# Reduce batch size in config.yaml
batch_size: 64 # or 32
```
2. **Mixed precision errors:**
```yaml
# Disable for older GPUs
mixed_precision: false
```
---
## 📜 License
This project is licensed under the **MIT License** - see [LICENSE.txt](LICENSE.txt) for details.
**TL;DR:** Use it freely for educational and commercial purposes! 🎉