--- license: mit task_categories: - text-classification language: - en size_categories: - 1M.png ``` Example: ``` A7kD3.png pQ82Lm.png ``` Thus, labels can be directly extracted from filenames without requiring an additional annotation file. ## Generation Methodology Images were generated using a synthetic rendering pipeline that includes: - Random font selection - Character position perturbations - Random background noise - Random line interference - Gaussian pixel noise This process improves robustness and helps OCR models generalize to real-world CAPTCHA images. ## Intended Use This dataset is suitable for: - Training deep learning OCR systems - CAPTCHA recognition research - Sequence recognition benchmarking - Synthetic data pretraining for document OCR systems - Curriculum learning before fine-tuning on real-world datasets ## Limitations - Images are synthetically generated and may not capture every real-world CAPTCHA style. - Domain adaptation may still be required for specific CAPTCHA systems. - Distribution of character sequences is random rather than language-based. ## Citation If you use this dataset in academic work, please cite: ``` Synthetic CAPTCHA OCR Dataset (1M), 2026 ```