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