CaptchaSolve30k / README.md
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---
license: apache-2.0
task_categories:
- other
language:
- en
tags:
- mouse-movement
- captcha
- biometrics
- human-behavior
- gaming
size_categories:
- 10K<n<100K
---
# CaptchaSolve30k - Human Mouse Movement Dataset
**The largest open-source dataset of human task-specific mouse trajectories by session count and unique participants, with 30,000 discrete sessions from thousands of users. The first and only open dataset of complete human captcha-solving interactions with full behavioral replays.**
Each session captures mouse/touch trajectories, timing data, and puzzle state at physics-tick resolution. Suitable for bot detection research, human-computer interaction studies, and ML model training.
## Dataset Overview
| Metric | Value |
|--------|-------|
| Total Sessions | 30,000 |
| Game Types | 3 |
| Physics Sample Rate | 240 Hz |
| Raw Input Sample Rate | 1000 Hz |
| Average Session Duration | 8-15 seconds |
### Privacy & Anonymization
Data was collected by Capycap, Inc. under a privacy policy disclosing collection practices, anonymization methods, and intended use for AI research. Users had the option to opt out of data storage.
- All coordinates are normalized to a 200×200 logical grid—raw screen coordinates were never stored
- Render size was randomized per session, removing device-specific signatures
- Sessions cannot be linked to individuals or correlated with each other
- No IP addresses, cookies, browser fingerprints, or persistent identifiers were collected
### Terms of Use
By downloading this dataset, you agree to the following:
1. **No re-identification**: Do not attempt to identify individuals, link sessions to specific users, or combine this data with other sources for identification purposes.
2. **No biometric profiling**: Do not use this data to build biometric identification systems, behavioral authentication, surveillance tools, or any system designed to identify or track individuals based on interaction patterns.
3. **Permitted uses**: This dataset is intended for AI research, bot detection, human-computer interaction studies, and training machine learning models (including generative models and automation agents).
4. **Redistribution**: If you redistribute this data or derivatives, you must include these same restrictions.
Violation of these terms may constitute a breach of contract and could result in legal liability.
## Game Types
### Sheep Herding
Guide sheep into a pen by drawing paths with your mouse. Tests continuous cursor control and path planning.
### Thread the Needle
Navigate through gaps without touching walls. Tests precision movement and spatial awareness.
### Polygon Stacking
Drag and stack polygon shapes on a platform. Tests click-drag interactions and placement accuracy.
## Data Format
Each session is a JSON object with the following fields:
| Field | Type | Description |
|-------|------|-------------|
| `index` | int | Session index in the dataset |
| `gameType` | string | One of: `sheep-herding`, `thread-the-needle`, `polygon-stacking` |
| `puzzleSeed` | int | Seed used to generate the puzzle (for reproducibility) |
| `duration` | int | Session duration in milliseconds |
| `physicsTickCount` | int | Number of physics ticks (at 240 Hz) |
| `tickInputs` | array | Input state at each physics tick |
| `inputStream` | string | Base64-encoded raw 1000 Hz mouse samples |
| `inputSampleCount` | int | Number of raw input samples |
| `touchscreen` | bool | Whether input was from a touchscreen |
### tickInputs Format
Array of objects representing input state at each 240 Hz physics tick:
```json
{
"x": 150.5, // X position in grid coordinates (0-200)
"y": 100.2, // Y position in grid coordinates (0-200)
"isDown": true, // Mouse button / touch pressed
"sampleIndex": 42 // Index into inputStream for this tick
}
```
### inputStream Format
Base64-encoded binary stream of raw 1000 Hz mouse samples. Each sample is 9 bytes:
- 4 bytes: X position (float32, little-endian)
- 4 bytes: Y position (float32, little-endian)
- 1 byte: Button state (0 = up, 1 = down)
## Usage
### Loading with Datasets Library
```python
from datasets import load_dataset
dataset = load_dataset("Capycap-AI/CaptchaSolve30k")
# Access a sample
sample = dataset['train'][0]
print(f"Game: {sample['gameType']}, Duration: {sample['duration']}ms")
```
### Decoding inputStream
```python
import base64
import struct
def decode_input_stream(b64_stream, sample_count):
data = base64.b64decode(b64_stream)
samples = []
for i in range(sample_count):
offset = i * 9
x, y = struct.unpack('<ff', data[offset:offset+8])
is_down = data[offset+8] == 1
samples.append({'x': x, 'y': y, 'isDown': is_down})
return samples
```
## Demo & Replay
Explore the dataset interactively with our demo space:
- **Visualize sessions**: Watch recorded mouse movements replay in real-time over the actual game
- **Inspect trajectories**: See exactly how humans navigate each puzzle type
- **Load samples**: Paste JSON from the dataset or load random samples directly
- **Play the games**: Try the puzzles yourself and export your own session data in the same format
[Open Demo Space](https://huggingface.co/spaces/Capycap-AI/CaptchaSolve-Demo)
## License
Apache License 2.0 - free for research and commercial use.
## Citation
```bibtex
@dataset{captchasolve30k,
title={CaptchaSolve30k: Human Mouse Movement Dataset},
author={Capycap Inc.},
year={2026},
url={https://huggingface.co/datasets/Capycap-AI/CaptchaSolve30k}
}
```
## Authors
This dataset was created by:
- **Tony Chen** - [GitHub](https://github.com/TonyChen06) | [LinkedIn](https://www.linkedin.com/in/tonychen06/)
- **James Gu** - [GitHub](https://github.com/jamesgu888) | [LinkedIn](https://www.linkedin.com/in/jamesgu888/)
- **Christopher He** - [LinkedIn](https://www.linkedin.com/in/chrismhe/)
- **Kevin Zhou** - [GitHub](https://github.com/zhoism) | [LinkedIn](https://www.linkedin.com/in/kevinzhou3/)
Published by Capycap Inc.
## Links
- [Demo Space](https://huggingface.co/spaces/Capycap-AI/CaptchaSolve-Demo)