captcha_data/README.md

---
license: mit
task_categories:
  - visual-question-answering
  - image-classification
  - video-classification
language:
  - en
tags:
  - captcha
  - visual-reasoning
  - spatial-reasoning
  - 3d-understanding
  - temporal-reasoning
  - benchmark
  - multimodal
size_categories:
  - 1K<n<10K
---

# NextGen-CAPTCHAs

A comprehensive benchmark dataset of next-generation CAPTCHA challenges designed to evaluate visual reasoning, spatial understanding, and temporal processing capabilities of AI systems.

## Dataset Description

This dataset contains **27 distinct CAPTCHA task types** with **3,037 total files** including images (PNG), animated GIFs, and ground truth annotations. The challenges span multiple cognitive dimensions including:

- **3D Spatial Reasoning**: Understanding objects from different viewpoints
- **Temporal Processing**: Tracking motion and changes over time
- **Visual Pattern Recognition**: Counting, matching, and identifying patterns
- **Logical Reasoning**: Following paths, folding shapes, understanding shadows

## Task Categories

| Category | Files | Media Type | Description |
|----------|-------|------------|-------------|
| **3D_Viewpoint** | 202 | Image | Match 3D wireframe objects viewed from different angles |
| **Backmost_Layer** | 110 | Image | Identify the backmost layer in overlapping shapes |
| **Box_Folding** | 333 | Image | Predict how 2D nets fold into 3D boxes |
| **Color_Counting** | 34 | Image | Count colors in sketches (grid selection) |
| **Dice_Roll_Path** | 23 | Image | Track dice faces after rolling along a path |
| **Dynamic_Jigsaw** | 201 | GIF | Complete jigsaw puzzles with animated pieces |
| **Hole_Counting** | 62 | Image | Count holes in topological shapes |
| **Illusory_Ribbons** | 82 | Image | Reason about illusory/impossible ribbon configurations |
| **Layered_Stack** | 62 | Image | Understand layered/stacked object arrangements |
| **Mirror** | 10 | Image | Identify correct mirror reflections |
| **Multi_Script** | 202 | Image | Recognize text across multiple writing systems |
| **Occluded_Pattern_Counting** | 52 | Image | Count patterns with partial occlusion |
| **Red_Dot** | 1 | Image | Track red dot position |
| **Rotation_Match** | 182 | Image | Match rotated shapes to reference |
| **Shadow_Direction** | 106 | Image | Determine shadow direction consistency |
| **Shadow_Plausible** | 32 | Image | Judge if shadows are physically plausible |
| **Spooky_Circle** | 21 | GIF | Track circles with spooky/illusory motion |
| **Spooky_Circle_Grid** | 22 | GIF | Grid-based spooky circle challenges |
| **Spooky_Jigsaw** | 381 | GIF | Jigsaw with illusory animated pieces |
| **Spooky_Shape_Grid** | 33 | GIF | Grid of shapes with illusory animations |
| **Spooky_Size** | 21 | GIF | Track size changes in illusory animations |
| **Spooky_Text** | 21 | GIF | Text with spooky/illusory effects |
| **Static_Jigsaw** | 3 | Image | Traditional static jigsaw puzzles |
| **Structure_From_Motion** | 22 | Image/Video | Infer 3D structure from motion cues |
| **Subway_Paths** | 102 | Image | Navigate complex subway/metro path networks |
| **Temporal_Object_Continuity** | 21 | Video | Track object identity through occlusions |
| **Trajectory_Recovery** | 81 | Image | Reconstruct movement trajectories |

## Data Format

Each task category contains:
- **Media files**: PNG images or GIF animations
- **ground_truth.json**: Annotations with prompts, options, and correct answers

### Example Ground Truth Entry

```json
{
  "color_counting_0000": {
    "prompt": "Click all sketches with 3 or fewer colors (ignore white)",
    "description": "Grid with 4 sketch(es) matching: less equal 3 colors",
    "options": ["6_bird_0.png", "6_car_0.png", "3_sun_1.png", ...],
    "answer": [2, 3, 7, 15],
    "grid_size": [4, 4],
    "difficulty": 5,
    "media_type": "image"
  }
}
```

## Statistics

- **Total Tasks**: 27 categories
- **Total Files**: 3,037
- **Image Files (PNG)**: 1,628
- **Animation Files (GIF)**: 1,367
- **Dataset Size**: ~897 MB

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("YaxinLuo/NextGen-CAPTCHAs")
```

Or download directly and load ground truth:

```python
import json

with open("Color_Counting/ground_truth.json") as f:
    challenges = json.load(f)

for challenge_id, data in challenges.items():
    print(f"Prompt: {data['prompt']}")
    print(f"Answer indices: {data['answer']}")
```

## Intended Use

This dataset is intended for:
- Benchmarking multimodal AI systems on visual reasoning tasks
- Research on spatial and temporal understanding in AI
- Evaluating robustness of vision-language models
- Studying CAPTCHA security against automated solvers

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{nextgen_captchas_2025,
  author = {Yaxin Luo},
  title = {NextGen-CAPTCHAs: A Benchmark for Visual Reasoning},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/YaxinLuo/NextGen-CAPTCHAs}
}
```

## License

apache-2.0