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---
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
``` |