Free OCR API

A powerful, free, and unlimited Optical Character Recognition (OCR) API that extracts text from images and PDFs. Built with TypeScript, Express.js, and Tesseract.js for reliable text extraction without any limits or hidden costs.

🚀 Features

No rate limits: Use as much as you want, whenever you want
Free and open source: No API keys, no subscriptions, no hidden fees
Multiple file types: Support for images (JPG, PNG, GIF, BMP, TIFF, WebP) and PDFs
Multi-language support: 13+ languages including English, Spanish, French, German, Chinese, Japanese, and more
Web interface: Simple drag-and-drop interface for testing
Fast processing: Built on optimized OCR technology
TypeScript: Fully typed for better development experience

📋 Supported File Types

Images

JPEG/JPG
PNG
GIF
BMP
TIFF
WebP

Documents

PDF (text extraction)

Languages (for images)

English (eng)
Spanish (spa)
French (fra)
German (deu)
Italian (ita)
Portuguese (por)
Russian (rus)
Chinese Simplified (chi_sim)
Chinese Traditional (chi_tra)
Japanese (jpn)
Korean (kor)
Arabic (ara)
Hindi (hin)

🛠️ Installation & Setup

Clone the repository:

git clone <repository-url>
cd ocr

Install dependencies:

npm install

Build the project:

npm run build

Start the server:

# Development mode
npm run dev

# Production mode
npm start

The server will start on http://localhost:3000

📖 API Documentation

Base URL

http://localhost:3000

Endpoints

Health Check

GET /health

Response:

{
  "status": "OK",
  "timestamp": "2025-09-28T20:48:19.632Z"
}

API Information

GET /api/info

Response:

{
  "supportedTypes": {
    "images": ["image/jpeg", "image/jpg", "image/png", "image/gif", "image/bmp", "image/tiff", "image/webp"],
    "documents": ["application/pdf"]
  },
  "supportedLanguages": ["eng", "spa", "fra", "deu", "ita", "por", "rus", "chi_sim", "chi_tra", "jpn", "kor", "ara", "hin"],
  "maxFileSize": "50MB"
}

OCR Processing

POST /api/ocr

Request:

Content-Type: multipart/form-data
Body:
- file: The image or PDF file to process
- language: (optional) Language code for OCR (default: 'eng', only applies to images)

Example using curl:

curl -X POST \
  -F "file=@example.jpg" \
  -F "language=eng" \
  http://localhost:3000/api/ocr

Success Response (200):

{
  "success": true,
  "data": {
    "text": "Extracted text from the file",
    "confidence": 95.5,
    "language": "eng",
    "processingTime": 1250
  },
  "fileInfo": {
    "originalName": "example.jpg",
    "mimeType": "image/jpeg",
    "size": 245760,
    "extension": ".jpg"
  }
}

Error Response (400/500):

{
  "error": "Validation Error",
  "message": "Unsupported file type: image/svg+xml"
}

🌐 Web Interface

Visit http://localhost:3000 to access the web interface for testing the API:

Drag & Drop: Simply drag files onto the upload area
File Browser: Click to browse and select files
Language Selection: Choose OCR language for image processing
Real-time Results: View extracted text immediately
Copy Function: One-click copy of extracted text

🔧 Tech Stack

Backend: Node.js + Express.js + TypeScript
OCR Engine: Tesseract.js (for images)
PDF Processing: pdf-parse
File Upload: Multer
Frontend: HTML5 + Tailwind CSS + Vanilla JavaScript

📝 Usage Examples

JavaScript/Node.js

const FormData = require('form-data');
const fs = require('fs');
const fetch = require('node-fetch');

async function extractText(filePath, language = 'eng') {
  const form = new FormData();
  form.append('file', fs.createReadStream(filePath));
  form.append('language', language);

  const response = await fetch('http://localhost:3000/api/ocr', {
    method: 'POST',
    body: form
  });

  const result = await response.json();
  return result.data.text;
}

// Usage
extractText('./document.jpg', 'eng')
  .then(text => console.log('Extracted:', text))
  .catch(err => console.error('Error:', err));

Python

import requests

def extract_text(file_path, language='eng'):
    with open(file_path, 'rb') as file:
        files = {'file': file}
        data = {'language': language}

        response = requests.post(
            'http://localhost:3000/api/ocr',
            files=files,
            data=data
        )

        if response.status_code == 200:
            return response.json()['data']['text']
        else:
            raise Exception(f"Error: {response.json()['message']}")

# Usage
try:
    text = extract_text('./document.jpg', 'eng')
    print(f"Extracted: {text}")
except Exception as e:
    print(f"Error: {e}")

cURL

# Extract text from an image
curl -X POST \
  -F "file=@document.jpg" \
  -F "language=eng" \
  http://localhost:3000/api/ocr

# Extract text from a PDF
curl -X POST \
  -F "file=@document.pdf" \
  http://localhost:3000/api/ocr

🚦 Error Handling

The API returns appropriate HTTP status codes and error messages:

400 Bad Request: Invalid file type, missing file, or file too large
500 Internal Server Error: Processing errors or server issues

Common error scenarios:

File size exceeds 50MB limit
Unsupported file format
Corrupted or invalid file
OCR processing failure

🔒 Security & Limitations

File Size Limit: 50MB maximum per file
Memory Usage: Files are processed in memory for security
No Persistence: Uploaded files are not stored on the server
CORS Enabled: API accessible from any origin

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

📄 License

This project is open source and available under the MIT License.

🆘 Support

Issues: Report bugs or request features via GitHub Issues
Documentation: Check this README for comprehensive API documentation
Testing: Use the web interface at http://localhost:3000 for quick testing

🎯 Use Cases

Document Digitization: Convert scanned documents to editable text
Receipt Processing: Extract data from receipts and invoices
Screenshot OCR: Convert screenshots to searchable text
Accessibility: Make images accessible with text extraction
Data Entry Automation: Automate manual data entry from images
Content Moderation: Extract text from images for content analysis
Archive Digitization: Convert old documents and photos to text

Made with ❤️ for the developer community. No limits, no costs, just OCR.

subhadeeproy3902/ocr