| --- |
| license: mit |
| task_categories: |
| - text-classification |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
|
|
| # Synthetic CAPTCHA OCR Dataset (1M) |
|
|
| ## Overview |
| This dataset contains **synthetically generated CAPTCHA images** designed for training and benchmarking Optical Character Recognition (OCR) models. Each image contains a randomly generated alphanumeric string rendered in CAPTCHA style with noise, distortions, and visual artifacts to simulate real-world conditions. |
|
|
| The dataset is created entirely using automated rendering pipelines and therefore contains perfectly accurate ground-truth labels. |
|
|
| --- |
|
|
| ## Dataset Characteristics |
|
|
| - **Dataset size:** 1,000,000 images |
| - **Image format:** PNG |
| - **Image resolution:** 160 × 60 pixels |
| - **Text length:** 5–10 characters |
| - **Character set:** |
| - Uppercase letters (A–Z) |
| - Lowercase letters (a–z) |
| - Digits (0–9) |
|
|
| Each file is named using the ground-truth label: |
|
|
| ``` |
| |
| <text>.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 |
| |
| ``` |
