projects/Dockers/ocr-api

OCR API

A FastAPI-based OCR (Optical Character Recognition) service that can extract text from PDF files and images using Tesseract.

Features

• PDF Processing: Convert PDF files to images and extract text using OCR
• Image Processing: Direct OCR processing of image files (PNG, JPG, JPEG, BMP, TIFF)
• Multi-language Support: Supports English, French, German, Spanish, and Italian
• Confidence Scoring: Returns confidence scores for OCR results
• RESTful API: Clean, documented API endpoints
• Docker Support: Easy deployment with Docker and Docker Compose
• File Validation: Built-in file type and size validation
• Background Processing: Efficient file cleanup and processing

Quick Start

Using Docker (Recommended)

1. Clone and build the container:

   docker-compose up --build
   

2. Access the API:

   • API Documentation: http://localhost:8000/docs
   • Health Check: http://localhost:8000/health

Local Development

1. Install system dependencies:

   # Ubuntu/Debian
   sudo apt-get update
   sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils libpoppler-cpp-dev pkg-config
   
   # macOS
   brew install tesseract poppler
   
   # Arch Linux
   sudo pacman -S tesseract tesseract-data-eng poppler
   

2. Install Python dependencies:

   pip install -r requirements.txt
   

3. Run the application:

   python main.py
   

API Endpoints

Health Check

• GET /health - Check if the service is running

PDF OCR

• POST /ocr/pdf - Extract text from PDF files
  • File: PDF file (max 50MB)
  • Response: Extracted text with metadata

Image OCR

• POST /ocr/image - Extract text from image files
  • File: Image file (PNG, JPG, JPEG, BMP, TIFF)
  • Response: Extracted text with metadata

Usage Examples

Using curl

Process a PDF file:

curl -X POST "http://localhost:8000/ocr/pdf" \
     -H "accept: application/json" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@document.pdf"

Process an image file:

curl -X POST "http://localhost:8000/ocr/image" \
     -H "accept: application/json" \
     -H "Content-Type: multipart/form-data" \
     -F "file=@image.png"

Using Python

import requests

# Process PDF
with open('document.pdf', 'rb') as f:
    files = {'file': f}
    response = requests.post('http://localhost:8000/ocr/pdf', files=files)
    result = response.json()
    print(f"Extracted text: {result['text']}")
    print(f"Processing time: {result['processing_time']}s")
    print(f"Confidence: {result['confidence']}%")

Using JavaScript/Fetch

// Process PDF
const formData = new FormData();
formData.append("file", fileInput.files[0]);

fetch("http://localhost:8000/ocr/pdf", {
  method: "POST",
  body: formData,
})
  .then((response) => response.json())
  .then((data) => {
    console.log("Extracted text:", data.text);
    console.log("Processing time:", data.processing_time);
    console.log("Confidence:", data.confidence);
  });

Response Format

{
  "text": "Extracted text content...",
  "pages": 3,
  "processing_time": 2.45,
  "confidence": 85.2
}

Configuration

Environment Variables

• PYTHONUNBUFFERED=1 - Ensures Python output is not buffered
• PYTHONDONTWRITEBYTECODE=1 - Prevents Python from writing .pyc files

Tesseract Configuration

The Docker image includes multiple language packs:

• English (eng)
• French (fra)
• German (deu)
• Spanish (spa)
• Italian (ita)

To add more languages, modify the Dockerfile and add additional tesseract-ocr-* packages.

Performance Tips

1. Image Quality: Higher DPI (300) provides better OCR accuracy but slower processing
2. File Size: Larger files take longer to process
3. Text Quality: Clear, high-contrast text yields better results
4. Language: Specify the correct language for better accuracy

Troubleshooting

Common Issues

1. Tesseract not found: Ensure Tesseract is installed on your system
2. PDF processing errors: Check if Poppler utilities are installed
3. Memory issues: Reduce DPI or process smaller files
4. Poor OCR quality: Ensure images are clear and have good contrast

Logs

The application logs processing information. Check Docker logs:

docker-compose logs ocr-api

Development

Project Structure

ocr-api/
├── main.py              # FastAPI application
├── requirements.txt     # Python dependencies
├── Dockerfile          # Docker configuration
├── docker-compose.yml  # Docker Compose configuration
└── README.md          # This file

Adding Features

1. New OCR engines: Modify the process_pdf_with_ocr function
2. Additional file formats: Add new endpoints and processing logic
3. Custom preprocessing: Add image preprocessing steps before OCR
4. Batch processing: Implement queue-based processing for multiple files

License

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