NNEngine/Captcha-OCR

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.

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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