CAPTCHA Image Dataset

About the Dataset

• Source: Data was web-scraped from a publicly available website, with CAPTCHAs solved and labeled using the 2Captcha API Service. Manual corrections were applied to ensure accuracy.
• Image Count: 10,001 CAPTCHA images (including one extra for good luck!).
• Types of CAPTCHA:
  • Black text on a grey background.
  • Grey text on a white background.
• Character Details:
  • Each CAPTCHA is exactly 6 characters long.
  • The dataset contains 21 unique characters used to create the CAPTCHA.
• Image Dimensions: Each CAPTCHA has a uniform resolution of 250×50 pixels.

Key Features

1. High-Quality Annotations: The dataset includes carefully labeled CAPTCHAs to ensure accurate model training.
2. Error Minimization: Misclassified or mislabeled data was manually corrected for improved reliability.
3. Pre-processed Data: Ready-to-use images for faster integration into machine learning pipelines.

Advantages of Using the CAPTCHA Image Dataset

1. Versatility: The dataset supports a wide range of tasks, including:
   • Image recognition and classification.
   • CAPTCHA-solving using neural networks.
   • Exploring OCR (Optical Character Recognition) models.
2. Learning Opportunities: Perfect for students, researchers, and developers to experiment with various machine learning architectures.
3. Adaptability: Can be used to train specialized models for different CAPTCHA styles using segmentation and classification.
4. Real-World Applications: Valuable for automation, web scraping, and bypassing CAPTCHA barriers ethically for research purposes.

Recommended Approaches for Model Training

• Convolutional Neural Network (CNN) with Binary Cross-Entropy (BCE) loss for classification.
• CNN-LSTM with Connectionist Temporal Classification (CTC) loss for sequence prediction.
• Convolutional Autoencoder followed by a simple NN-classifier using latent representations for solving CAPTCHAs.

Ideas for Future Use

• Image segmentation to classify CAPTCHA types for specialized training.
• Character-wise segmentation for enhanced OCR accuracy.
• Building multilingual CAPTCHA solvers for diverse datasets.

Important Considerations

While the dataset is curated with attention to detail, minor misclassifications may occur due to human errors in CAPTCHA labeling. Use this dataset responsibly and always follow ethical web-scraping practices.