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https://github.com/robertknight/tesseract-wasm

JS/WebAssembly build of the Tesseract OCR engine for use in browsers and Node
https://github.com/robertknight/tesseract-wasm

js ocr webassembly

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JS/WebAssembly build of the Tesseract OCR engine for use in browsers and Node

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README 

        
# tesseract-wasm



![npm package](https://img.shields.io/npm/v/tesseract-wasm)



A WebAssembly build of the [Tesseract](https://github.com/tesseract-ocr/tesseract)

OCR engine for use in the browser and Node.



tesseract-wasm can detect and recognize text in document images. It supports multiple languages via different [trained models](https://tesseract-ocr.github.io/tessdoc/Data-Files).



👉 [**Try the demo**](https://robertknight.github.io/tesseract-wasm/) (Currently supports English)



## Features



This Tesseract build has been optimized for use in the browser by:



- Stripping functionality which is not needed in a browser environment (eg.

  code to parse various image formats) to reduce download size and improve

  startup performance. The library and English training data require a ~2.1MB

  download (with Brotli compression).



- Using [WebAssembly SIMD](https://v8.dev/features/simd) when available

  (Chrome >= 91, Firefox >= 90, Safari >= 16.4) to improve text

  recognition performance.



- Providing a high-level API that can be used to run web pages without blocking

  interaction and a low-level API that provides more control over execution.



## Setup



1. Add the tesseract-wasm library to your project:



   ```sh

   npm install tesseract-wasm

   ```



2. Serve the `tesseract-core.wasm`, `tesseract-core-fallback.wasm` and

   `tesseract-worker.js` files from `node_modules/tesseract-wasm/dist` alongside

   your JavaScript bundle.



3. Get the training data file(s) for the languages you want to support from the

   [tessdata_fast](https://github.com/tesseract-ocr/tessdata_fast) repo and

   serve it from a URL that your JavaScript can load. The `eng.traineddata`

   file supports English for example, and also works with many documents in

   other languages that use the same script.



## Usage



tesseract-wasm provides two APIs: a high-level asynchronous API (`OCRClient`)

and a lower-level synchronous API (`OCREngine`). The high-level API is the most

convenient way to run OCR on an image in a web page. It handles running the OCR

engine inside a Web Worker to avoid blocking page interaction. The low-level API

is useful if more control is needed over where/how the code runs and has lower

latency per API call.



### Using OCRClient in a web page



```js

import { OCRClient } from 'tesseract-wasm';



async function runOCR() {

  // Fetch document image and decode it into an ImageBitmap.

  const imageResponse = await fetch('./test-image.jpg');

  const imageBlob = await imageResponse.blob();

  const image = await createImageBitmap(imageBlob);



  // Initialize the OCR engine. This will start a Web Worker to do the

  // work in the background.

  const ocr = new OCRClient();



  try {

    // Load the appropriate OCR training data for the image(s) we want to

    // process.

    await ocr.loadModel('eng.traineddata');



    await ocr.loadImage(image);



    // Perform text recognition and return text in reading order.

    const text = await ocr.getText();



    console.log('OCR text: ', text);

  } finally {

    // Once all OCR-ing has been done, shut down the Web Worker and free up

    // resources.

    ocr.destroy();

  }

}



runOCR();

```



## Examples and documentation



See the `examples/` directory for projects that show usage of the library in

the browser and Node.



See the [API documentation](https://robertknight.github.io/tesseract-wasm/api/)

for detailed usage information.



See the Tesseract [User Manual](https://tesseract-ocr.github.io/tessdoc/) for

information on how Tesseract works, as well as advice on [improving

recognition](https://tesseract-ocr.github.io/tessdoc/ImproveQuality.html).



## Development



### Prerequisites



To build this library locally, you will need:



 - A C++ build toolchain (eg. via the `build-essential` package on Ubuntu or Xcode on macOS)

 - [CMake](https://cmake.org)

 - [Ninja](https://ninja-build.org)



The [Emscripten](https://emscripten.org) toolchain used to compile C++ to

WebAssembly is downloaded as part of the build process.



To install CMake and Ninja:



#### On macOS:



```

brew install cmake ninja

```



#### On Ubuntu



```

sudo apt-get install cmake ninja-build

```



### Building the library



```sh

git clone https://github.com/robertknight/tesseract-wasm

cd tesseract-wasm



# Build WebAssembly binaries and JS library in dist/ folder

make lib



# Run tests

make test

```



To test your local build of the library with the example projects, or your own

projects, you can use [yalc](https://www.npmjs.com/package/yalc).



```sh

# In this project

yalc publish



# In the project where you want to use your local build of tesseract-wasm

yalc link tesseract-wasm

```