vietcombank-captcha 0.1.0

Lightweight CAPTCHA predictor for Vietcombank using ONNX

vietcombank-captcha

A lightweight Python library for solving Vietcombank CAPTCHA images using ONNX runtime. This project is purely educational. Using synthetic data from various sources.

Features

• 🚀 Fast inference using ONNX runtime
• 🎯 High accuracy (>95% on test set)
• 🔧 Simple API with minimal dependencies
• 📦 Lightweight package (~2.2MB model)
• 🖼️ Supports multiple input formats (file path, PIL Image, numpy array)
• 🔢 Batch prediction support
• 🍎 Optimized for Apple M Chip

Installation

Using pip

pip install vietcombank-captcha

Using UV (recommended)

uv pip install vietcombank-captcha

Quick Start

Basic Usage

from vietcombank_captcha import predict

# Predict from image file
captcha_code = predict("captcha.png")
print(f"Predicted code: {captcha_code}")

Advanced Usage

from vietcombank_captcha import VietcombankCaptcha

# Initialize predictor
predictor = VietcombankCaptcha()

# Predict single image with confidence scores
code, confidences = predictor.predict_with_confidence("captcha.png")
print(f"Code: {code}")
print(f"Confidence per digit: {confidences}")

# Batch prediction
images = ["captcha1.png", "captcha2.png", "captcha3.png"]
results = predictor.predict_batch(images)
print(f"Results: {results}")

# With confidence scores
results_with_conf = predictor.predict_batch(images, return_confidence=True)
for code, conf in results_with_conf:
    print(f"Code: {code}, Avg confidence: {sum(conf)/len(conf):.2f}")

Using PIL Image

from PIL import Image
from vietcombank_captcha import predict

# Load image with PIL
img = Image.open("captcha.png")
code = predict(img)
print(f"Code: {code}")

Using numpy array

import numpy as np
from vietcombank_captcha import predict

# From numpy array (H, W, 3) RGB format
img_array = np.array(...)  # Your image array
code = predict(img_array)
print(f"Code: {code}")

Command Line Interface

# Predict single image
vietcombank-captcha predict image.png

# Predict with confidence scores
vietcombank-captcha predict image.png --confidence

# Batch prediction
vietcombank-captcha predict-batch ./captcha_folder/

# Use custom model
vietcombank-captcha predict image.png --model custom_model.onnx

API Reference

VietcombankCaptcha

Main predictor class.

__init__(model_path: Optional[str] = None)

Initialize the predictor with an optional custom model path.

predict(image) -> str

Predict CAPTCHA code from an image.

predict_with_confidence(image) -> Tuple[str, List[float]]

Predict with confidence scores for each digit.

predict_batch(images, return_confidence=False)

Predict multiple images at once.

predict(image, model_path=None) -> str

Convenience function for single prediction.

Requirements

• Python >= 3.8
• numpy >= 1.20.0
• Pillow >= 9.0.0
• onnxruntime >= 1.16.0

Model Information

• Input: RGB image (155x50 pixels)
• Output: 5-digit code (0-9)
• Model size: ~2.2MB
• Architecture: Multi-output CNN optimized for CAPTCHA recognition

Performance

• Inference time: ~5-10ms per image (CPU)
• Accuracy: >95% on test dataset
• Memory usage: <100MB

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

For issues and questions, please use the GitHub issue tracker.