automeris-io/WebPlotDigitizer

Computer vision assisted tool to extract numerical data from plot images.

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automeris-io/WebPlotDigitizer has 3k+ stars and 400+ forks on GitHub, is written in JavaScript, and was last pushed on Jul 27, 2026. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 34 minutes ago
Added to GitGenius on September 21st, 2026
Created on September 25th, 2010
Open Issues & Pull Requests: 104 (+0)
GitHub issues: Enabled
Number of forks: 439
Total Stargazers: 3,176 (+0)
Total Subscribers: 72 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 2.1 days
Mean response time: 258.7 days
90th percentile: 1209.1 days
Tracked items: 5

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Issue Activity (beta)

Open issues: 10
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 550 days
Stale 30+ days: 10
Stale 90+ days: 9

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

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Detailed Description

WebPlotDigitizer is a computer vision assisted tool that extracts numerical data from images of charts, graphs, and other data visualizations.

The tool solves the problem of recovering quantitative data trapped in visual form—plots in papers, reports, or other documents where the underlying numbers are unavailable. It uses computer vision techniques to identify plot elements, axes, and data points, then converts them into extractable numerical values. Users upload or provide images of visualizations and the tool assists in digitizing the data through a web interface.

WebPlotDigitizer suits researchers, academics, and industry professionals who need to recover data from published figures or historical charts. It works with a variety of visualization types and has been applied across scientific and technical domains. The tool is available as a web application requiring sign-up on the project's homepage, with both free and potentially premium features including cloud-based AI assistance for more complex extraction tasks.

Development of the tool is community-conscious but controlled. The maintainer requests consultation before accepting contributions and does not publish an official roadmap, indicating a deliberate approach to feature direction. The frontend code is open source under the GNU AGPL v3 license, while cloud-based systems and AI assistance features remain proprietary. Documentation is maintained separately on the project website.