tesseract.js vs ocr-space-api-wrapper
OCR (Optical Character Recognition) Libraries Comparison
1 Year
tesseract.jsocr-space-api-wrapper
What's OCR (Optical Character Recognition) Libraries?

OCR libraries are essential tools in web development for converting different types of documents, such as scanned paper documents, PDFs, or images, into editable and searchable data. These libraries utilize advanced algorithms to recognize and extract text from images, enabling applications to process and analyze textual information efficiently. The choice of an OCR library can significantly impact the accuracy, speed, and ease of integration into your web applications, making it crucial to understand the features and capabilities of each option.

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tesseract.js181,54236,3701.41 MB338 days agoApache-2.0
ocr-space-api-wrapper13,10527457 kB06 months agoMIT
Feature Comparison: tesseract.js vs ocr-space-api-wrapper

Processing Method

  • tesseract.js:

    tesseract.js operates entirely in the browser or on a server, processing images locally. This allows for greater control over the OCR process and eliminates the need for internet access, making it suitable for applications that require offline functionality.

  • ocr-space-api-wrapper:

    ocr-space-api-wrapper utilizes an external API to perform OCR processing. This means that images are sent to the OCR.space service, which processes them and returns the extracted text. This approach simplifies implementation but relies on internet connectivity and the performance of the external service.

Ease of Use

  • tesseract.js:

    tesseract.js has a steeper learning curve compared to ocr-space-api-wrapper due to its extensive configuration options and the need to manage local processing. However, it offers more flexibility and customization for advanced users who need specific OCR functionalities.

  • ocr-space-api-wrapper:

    ocr-space-api-wrapper is designed for ease of use, with a simple API that allows developers to quickly integrate OCR capabilities into their applications. It requires minimal setup, making it accessible for developers who want to implement OCR without delving into complex configurations.

Performance

  • tesseract.js:

    tesseract.js can deliver high performance for OCR tasks, especially when optimized. It processes images locally, which can lead to faster results for applications that require immediate feedback, although performance may vary based on the complexity of the images being processed.

  • ocr-space-api-wrapper:

    The performance of ocr-space-api-wrapper is dependent on the speed of the external API and the quality of the internet connection. While it can handle a variety of image formats, latency may be an issue for applications requiring real-time processing.

Accuracy

  • tesseract.js:

    tesseract.js is known for its high accuracy in text recognition, especially when properly configured and trained with the right language data. It can handle various fonts and layouts, making it a robust choice for diverse OCR needs.

  • ocr-space-api-wrapper:

    The accuracy of text extraction using ocr-space-api-wrapper largely depends on the external API's capabilities and the quality of the images submitted. It generally performs well with clear images but may struggle with poor quality or complex layouts.

Cost

  • tesseract.js:

    tesseract.js is open-source and free to use, making it an attractive option for developers looking to implement OCR without incurring additional costs. However, it may require more resources for setup and maintenance.

  • ocr-space-api-wrapper:

    ocr-space-api-wrapper operates on a freemium model, where basic usage may be free, but extensive use or advanced features may incur costs. This can be a consideration for projects with budget constraints.

How to Choose: tesseract.js vs ocr-space-api-wrapper
  • tesseract.js:

    Choose tesseract.js if you need a powerful, open-source OCR solution that runs directly in the browser or on Node.js. It is ideal for projects that require offline capabilities, customization, and control over the OCR process without relying on external services.

  • ocr-space-api-wrapper:

    Choose ocr-space-api-wrapper if you prefer a simple, straightforward API that leverages an external OCR service. It is suitable for projects where you want to avoid the complexities of local processing and are comfortable with sending data to a third-party service for text extraction.

README for tesseract.js

Tesseract.js

Lint & Test CodeQL Gitpod Ready-to-Code Financial Contributors on Open Collective npm version Maintenance License Code Style npm jsDelivr hits (npm)

Tesseract.js is a javascript library that gets words in almost any language out of images. (Demo)

Image Recognition

fancy demo gif

Video Real-time Recognition

Tesseract.js Video

Tesseract.js works in the browser using webpack, esm, or plain script tags with a CDN and on the server with Node.js. After you install it, using it is as simple as:

import { createWorker } from 'tesseract.js';

(async () => {
  const worker = await createWorker('eng');
  const ret = await worker.recognize('https://tesseract.projectnaptha.com/img/eng_bw.png');
  console.log(ret.data.text);
  await worker.terminate();
})();

When recognizing multiple images, users should create a worker once, run worker.recognize for each image, and then run worker.terminate() once at the end (rather than running the above snippet for every image).

Installation

Tesseract.js works with a <script> tag via local copy or CDN, with webpack via npm and on Node.js with npm/yarn.

CDN

<!-- v5 -->
<script src='https://cdn.jsdelivr.net/npm/tesseract.js@5/dist/tesseract.min.js'></script>

After including the script the Tesseract variable will be globally available and a worker can be created using Tesseract.createWorker.

Alternatively, an ESM build (used with import syntax) can be found at https://cdn.jsdelivr.net/npm/tesseract.js@5/dist/tesseract.esm.min.js.

Node.js

Requires Node.js v14 or higher

# For latest version
npm install tesseract.js
yarn add tesseract.js

# For old versions
npm install tesseract.js@3.0.3
yarn add tesseract.js@3.0.3

Project Scope

Tesseract.js aims to bring the Tesseract OCR engine (a separate project) to the browser and Node.js, and works by wrapping a WebAssembly port of Tesseract. This project does not modify core Tesseract features. Most notably, Tesseract.js does not support PDF files and does not modify the Tesseract recognition model to improve accuracy.

If your project requires features outside of this scope, consider the Scribe.js library. Scribe.js is an alternative library created to accommodate common feature requests that are outside of the scope of this repo. Scribe.js includes improvements to the Tesseract recognition model and supports extracting text from PDF documents, among other features. For more information see Scribe.js vs. Tesseract.js.

Documentation

Community Projects and Examples

The following are examples and projects built by the community using Tesseract.js. Officially supported examples are found in the examples directory.

  • Projects
    • Scribe OCR: web application for scanning documents (images and PDFs)
    • Chrome Extension (with Manifest V3): https://github.com/Tshetrim/Image-To-Text-OCR-extension-for-ChatGPT
  • Examples
    • Converting PDF to text: https://github.com/racosa/pdf2text-ocr
    • Use blocks output to generate granular data [word/symbol level]: https://github.com/Kishlay-notabot/tesseract-bbox-examples
    • Electron: https://github.com/Balearica/tesseract.js-electron
    • Typescript: https://github.com/Balearica/tesseract.js-typescript

If you have a project or example repo that uses Tesseract.js, feel free to add it to this list using a pull request. Examples submitted should be well documented such that new users can run them; projects should be functional and actively maintained.

Major changes in v6

Version 6 changes are documented in this issue. Highlights are below.

  • Fixed memory leak in previous versions
  • Overall reductions in runtime and memory usage
  • Breaking changes:
    • All outputs formats other than text are disabled by default.
      • To re-enable the hocr output (for example), set the following: worker.recognize(image, {}, { hocr: true })
    • Minor changes to the structure of the JavaScript object (blocks) output
    • See this issue for full list

Major changes in v5

Version 5 changes are documented in this issue. Highlights are below.

  • Significantly smaller files by default (54% smaller for English, 73% smaller for Chinese)
    • This results in a ~50% reduction in runtime for first-time users (who do not have the files cached yet)
  • Significantly lower memory usage
  • Breaking changes:
    • createWorker arguments changed
      • Setting non-default language and OEM now happens in createWorker
        • E.g. createWorker("chi_sim", 1)
    • worker.initialize and worker.loadLanguage functions should be deleted from code
    • See this issue for full list

Upgrading from v2 to v5? See this guide.

Major changes in v4

Version 4 includes many new features and bug fixes--see this issue for a full list. Several highlights are below.

  • Added rotation preprocessing options (including auto-rotate) for significantly better accuracy
  • Processed images (rotated, grayscale, binary) can now be retrieved
  • Improved support for parallel processing (schedulers)
  • Breaking changes:
    • createWorker is now async
    • getPDF function replaced by pdf recognize option

Contributing

Development

To run a development copy of Tesseract.js do the following:

# First we clone the repository
git clone https://github.com/naptha/tesseract.js.git
cd tesseract.js

# Then we install the dependencies
npm install

# And finally we start the development server
npm start

The development server will be available at http://localhost:3000/examples/browser/basic-efficient.html in your favorite browser. It will automatically rebuild tesseract.min.js and worker.min.js when you change files in the src folder.

Building Static Files

To build the compiled static files just execute the following:

npm run build

This will output the files into the dist directory.

Run Tests

Always confirm the automated tests pass before submitting a pull request. To run the automated tests locally, run the following commands.

npm run lint
npm run test

Contributors

Code Contributors

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Individuals

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