naptha/tesseract.js

Pure Javascript OCR for more than 100 Languages 📖🎉🖥

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Summary Information

Updated 52 minutes ago
Added to GitGenius on August 31st, 2026
Created on June 24th, 2015
Open Issues & Pull Requests: 49 (+0)
Number of forks: 2,390
Total Stargazers: 38,684 (+1)
Total Subscribers: 482 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 14.4 hours
Mean response time: 47.6 days
90th percentile: 161.1 days
Tracked items: 64

How this project is maintained

Around half of the issues opened in the past year never receive a reply. Only 11% of issues opened in the past year have been closed. Three people close 85% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 17
New in 7 days: 1
Closed in 7 days: 0
Avg open age: 638 days
Stale 30+ days: 16
Stale 90+ days: 13

Recent activity

Opened in 7 days: 1
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • recognition accuracy (4)
  • breaking change (1)
  • dependency bug (1)
  • language data (1)

Detailed Description

Tesseract.js is a JavaScript library that performs optical character recognition on images in the browser and Node.js using WebAssembly.

The tool solves the problem of extracting text from images across more than one hundred languages without requiring server-side processing. It works by wrapping a WebAssembly port of the Tesseract OCR engine, allowing recognition to run entirely client-side. Users create a worker, call recognize on image data, and terminate the worker when done. The library supports multiple deployment patterns: it can be loaded via CDN with a script tag, bundled with webpack, imported as an ES module, or run on Node.js.

Tesseract.js suits projects where client-side text extraction is acceptable and server infrastructure for OCR is unavailable or undesirable. It works with images in various formats and handles real-time video recognition through a companion library. Developers should be aware that the project explicitly does not support PDF files and does not modify Tesseract's recognition model to improve accuracy beyond what the underlying engine provides. The tool is a wrapper around existing Tesseract functionality rather than an enhancement to it.

The project maintains active continuous integration with linting and testing workflows, performs static analysis through CodeQL checks, and receives financial contributions through an open collective. Development follows the Airbnb code style standard.