README.md

metadata

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:

{
  "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

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

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

License

Apache License 2.0 - free for research and commercial use.

Citation

@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 | LinkedIn
• James Gu - GitHub | LinkedIn
• Christopher He - LinkedIn
• Kevin Zhou - GitHub | LinkedIn

Published by Capycap Inc.

Links

• Demo Space