You’re all set — your build and tests passed. Now it depends on what you want to do next. Pick one:
1) Try OCR on a real image (Node.js)
- In a new folder (your app), install the lib:
- mkdir my-ocr && cd my-ocr
- npm init -y
- npm i tesseract.js
- Create demo.mjs:
- import { createWorker } from 'tesseract.js';
const run = async () => {
const worker = await createWorker('eng', undefined, { logger: m => console.log(m) }); // optional progress
const { data: { text } } = await worker.recognize('https://tesseract.projectnaptha.com/img/eng_bw.png'); // or a local path like 'C:/path/to/image.png'
console.log('RESULT:\n', text);
await worker.terminate();
};
run();
- Run it:
- node demo.mjs
2) Quick test in the browser (no bundler)
- Make an index.html:
-
- Open it in your browser and check the console.
3) Run the included examples from the repo you cloned
- Start the dev server:
- npm start
- Open this in your browser:
- http://localhost:3000/examples/browser/basic-efficient.html
- Try other example pages under /examples too.
4) Build the distributables
- npm run build
- Outputs go to dist/ (tesseract.min.js and worker.min.js)
5) Contribute a change
- Make your edits in src/
- Run:
- npm run lint
- npm run test
- Commit on a new branch and open a PR on GitHub.
Extras you might want soon
- Different language: const worker = await createWorker('chi_sim'); // or 'eng+spa'
- Get JSON block/word data (v6 defaults to text only):
- const ret = await worker.recognize(img, {}, { blocks: true });
- console.log(ret.data.blocks);
- Parallelize multiple images:
- Use createScheduler and multiple workers.
Tell me which path you want to take (Node script, browser page, examples, or contributing), and I’ll walk you through it step-by-step. explain to me in detail using scaffolding method n oso explain all the terms i cant understand
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Welcome to our comprehensive guide on OCR technology and Tesseract.js. OCR, or Optical Character Recognition, is a powerful technology that automatically extracts text from images. Think of it as giving computers the ability to read text just like humans do. Tesseract.js is a JavaScript library that brings this capability to web browsers and Node.js applications. We'll use a scaffolding approach to build your understanding step by step, starting with basic concepts and gradually moving to advanced implementations.
Now let's set up our development environment using the scaffolding method. First, we need Node.js, which is a JavaScript runtime that allows us to run JavaScript outside the browser. npm is Node's package manager that helps us install and manage libraries. We start by creating a new directory called my-ocr, then navigate into it. The npm init command creates a package.json file that describes our project and its dependencies. Finally, we install tesseract.js using npm install. This scaffolding approach ensures we have a solid foundation before writing any code.
Let's implement our OCR application step by step using scaffolding. First, we import the createWorker function from tesseract.js using ES6 module syntax. Then we create an async function called run, which allows us to use await for handling promises. Inside the function, we create a worker for English language recognition. The worker.recognize method processes our image and returns the extracted text. We use destructuring to get the text from the response data. Finally, we terminate the worker to free up resources. This scaffolding approach builds from basic imports to complete functionality, making each step clear and manageable.
Now let's extend our Node.js knowledge to browser implementation using scaffolding. The core OCR logic remains the same, but the environment changes. Instead of importing modules, we load Tesseract.js via CDN script tag. The HTML structure provides the foundation, while JavaScript handles the OCR processing. Notice how we use Tesseract.createWorker instead of the imported createWorker function. The async/await pattern stays identical, and results appear in the browser console instead of terminal. This scaffolding approach shows how the same concepts apply across different environments, building confidence through familiar patterns.
Now let's explore advanced OCR features using our scaffolding approach. Building from basic text extraction, we can specify different languages like Chinese simplified or combine multiple languages like English plus Spanish. The next level involves extracting detailed JSON data with block and word positions, not just plain text. For performance optimization, we can use createScheduler with multiple workers to process images in parallel, achieving up to twice the speed. This scaffolding progression shows how to evolve from simple OCR to sophisticated text analysis systems. Each level builds on the previous foundation, making complex features manageable and understandable.