# Tesseract.js — Pure JavaScript OCR for 100+ Languages

> Run optical character recognition entirely in the browser or Node.js with no native dependencies, supporting over 100 languages via a WebAssembly port of the Tesseract engine.

## Install

Save as a script file and run:

# Tesseract.js — Pure JavaScript OCR for 100+ Languages

## Quick Use
```bash
npm install tesseract.js

# In Node.js
node -e "
const { createWorker } = require('tesseract.js');
(async () => {
  const worker = await createWorker('eng');
  const { data: { text } } = await worker.recognize('image.png');
  console.log(text);
  await worker.terminate();
})();
"
```

## Introduction
Tesseract.js brings the Tesseract OCR engine to JavaScript through WebAssembly, enabling text extraction from images directly in browsers and Node.js environments. It eliminates the need for server-side OCR services or native binary installations, making document processing accessible to any JavaScript application.

## What Tesseract.js Does
- Extracts printed text from images in over 100 languages including CJK scripts
- Runs entirely client-side in the browser via WebAssembly with no server round-trips
- Provides word-level bounding boxes, confidence scores, and paragraph segmentation
- Supports multiple workers for parallel OCR processing of batch images
- Detects page orientation and script direction automatically

## Architecture Overview
Tesseract.js wraps the C++ Tesseract engine compiled to WebAssembly via Emscripten. A scheduler distributes recognition tasks across web workers (browser) or worker threads (Node.js), each loading trained language data files on demand. The library exposes a Promise-based API that handles worker lifecycle, memory management, and data transfer transparently.

## Self-Hosting & Configuration
- Install via npm or load from a CDN script tag for browser use
- Language data files are fetched from a CDN by default; set `workerPath` and `langPath` to serve them from your own origin
- Configure `tessedit_pageseg_mode` to optimize for single lines, blocks, or full pages
- Use `worker.setParameters` to whitelist character sets or switch OCR engine modes (legacy vs LSTM)
- Pre-load language data with `worker.loadLanguage` to avoid latency at recognition time

## Key Features
- Zero native dependencies: pure JS and WASM, works in any modern browser or Node.js
- Scheduler API for parallel processing across multiple workers
- Incremental progress callbacks for real-time UI feedback during recognition
- Supports combined language models (e.g., eng+fra) for multilingual documents
- Compatible with image inputs from Canvas, File, Buffer, URL, or base64

## Comparison with Similar Tools
- **Google Cloud Vision** — cloud-hosted with higher accuracy on complex layouts, but requires API keys and per-request billing
- **Paddle OCR** — Python-based with strong CJK accuracy; heavier runtime, not browser-native
- **EasyOCR** — Python library with GPU support; better for server pipelines, not for client-side use
- **Amazon Textract** — managed service with table and form extraction; vendor lock-in and cost at scale

## FAQ
**Q: How accurate is Tesseract.js compared to cloud OCR services?**
A: For clean printed text, accuracy is comparable. For complex layouts, handwriting, or low-quality scans, cloud services with custom models tend to perform better.

**Q: Can it run in a web browser without a server?**
A: Yes. The entire engine runs client-side via WebAssembly. Language data is fetched once and can be cached.

**Q: Does it support PDF input?**
A: Not directly. Convert PDF pages to images first using a library like pdf.js, then pass the images to Tesseract.js.

**Q: How large are the language data files?**
A: The English trained data file is roughly 4 MB (gzipped). CJK languages are larger, around 10-20 MB each.

## Sources
- https://github.com/naptha/tesseract.js
- https://tesseract.projectnaptha.com/

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Source: https://tokrepo.com/en/workflows/asset-d5db39a9
Author: Script Depot