Document Text Extractor API

A powerful FastAPI-based service for extracting text from various document formats including PDFs, images, and Microsoft Office documents. The service uses OCR technology and native parsers to handle multiple file types efficiently.

Features

• PDF Text Extraction: Extract text from PDF files using native parsing and OCR fallback for scanned PDFs
• Image OCR: Extract text from images using Tesseract OCR engine
• Office Document Support: Extract text from Microsoft Office documents (Word, Excel, PowerPoint)
• Multiple Image Formats: Support for JPEG, PNG, GIF, WebP image formats
• Web UI: User-friendly drag-and-drop interface for easy document uploads
• File Size Limits: Configurable file size validation (default 50MB)
• Docker Ready: Containerized application with Docker and Docker Compose support
• Health Check: Built-in health monitoring endpoint
• REST API: Clean RESTful API with automatic documentation
• CORS Enabled: Cross-origin resource sharing for frontend integration

Supported File Formats

Category	Formats	Method
PDF	.pdf	pdfplumber + OCR fallback
Images	.jpg, .jpeg, .png, .gif, .webp	Tesseract OCR
Office	.docx, .xlsx, .pptx	Native parsers

Quick Start

Using Docker Compose (Recommended)

1. Clone the repository:

git clone <repository-url>
cd document-text-extractor

2. Start the service:

docker-compose up -d

3. Access the application:
   • Web UI: http://localhost:8000 - User-friendly interface for document uploads
   • API Documentation: http://localhost:8000/docs - Interactive API documentation
   • Health Check: http://localhost:8000/health - Service status

Manual Installation

1. Install system dependencies:

# Ubuntu/Debian
sudo apt-get update
sudo apt-get install tesseract-ocr libtesseract-dev poppler-utils

# macOS
brew install tesseract poppler

# Windows
# Download and install Tesseract from: https://github.com/UB-Mannheim/tesseract/wiki

2. Install Python dependencies:

pip install -r requirements.txt

3. Run the application:

uvicorn app.main:app --host 0.0.0.0 --port 8000

Running from PyCharm

Setup Run Configuration:

1. Open Run → Edit Configurations...
2. Click + → Python
3. Configure as follows:
   • Name: FastAPI - Document Extractor
   • Module name: (select radio button) → uvicorn
   • Parameters: app.main:app --reload --host 0.0.0.0 --port 8000
   • Working directory: /path/to/document-text-extractor
   • Environment variables: MAX_FILE_SIZE_MB=50;TESSERACT_CMD=tesseract
4. Click OK and run with the green play button

Alternative - Using Script:

Create run.py in the project root:

import uvicorn

if __name__ == "__main__":
    uvicorn.run("app.main:app", host="0.0.0.0", port=8000, reload=True)

Then set Script path to run.py in the run configuration.

API Documentation

Once the service is running, visit:

• Interactive API Documentation: http://localhost:8000/docs
• Alternative Documentation: http://localhost:8000/redoc

Web UI

The application includes a user-friendly web interface for easy document text extraction:

Accessing the Web UI

Open your browser and navigate to:

http://localhost:8000

Features

• Drag-and-Drop Upload: Simply drag your file onto the upload zone or click to browse
• Supported Formats Display: Clear indication of all supported file formats
• Client-Side Validation: Instant feedback on file size and format before upload
• Progress Indicator: Visual feedback during text extraction
• Result Display: View extracted text in a textarea with character count
• Copy to Clipboard: One-click copy of extracted text
• Download as TXT: Save extracted text as a text file
• Specific Error Messages: Clear error feedback for troubleshooting

Supported Formats

The Web UI supports the following file types:

• PDF: .pdf
• Office Documents: .docx, .xlsx, .pptx
• Images: .jpg, .jpeg, .png, .gif, .webp

File Size Limits

• Default Maximum Size: 50 MB
• Configurable: Can be adjusted via environment variable (see Configuration section)

API Endpoints

Health Check

GET /health

Returns the service status.

PDF Text Extraction

POST /extract/pdf
Content-Type: multipart/form-data

file: <PDF_FILE>

Image Text Extraction (OCR)

POST /extract/image
Content-Type: multipart/form-data

file: <IMAGE_FILE>

Office Document Text Extraction

POST /extract/office
Content-Type: multipart/form-data

file: <OFFICE_FILE>

Usage Examples

Using cURL

Extract text from PDF:

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

Extract text from image:

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

Extract text from Office document:

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

Using Python Requests

import requests

# Extract text from PDF
with open('document.pdf', 'rb') as f:
    response = requests.post(
        'http://localhost:8000/extract/pdf',
        files={'file': f}
    )
    text = response.json()['text']
    print(text)

Project Structure

document-text-extractor/
├── app/
│   └── main.py          # FastAPI application and endpoints
├── static/
│   ├── index.html       # Web UI interface
│   └── app.js          # Client-side JavaScript logic
├── docker-compose.yml   # Docker Compose configuration
├── Dockerfile          # Docker image definition
├── requirements.txt    # Python dependencies
└── README.md          # Project documentation

Dependencies

Core Libraries

• FastAPI: Modern web framework for building APIs
• uvicorn: ASGI server for running FastAPI
• pytesseract: Python wrapper for Tesseract OCR
• Pillow: Image processing library
• pdfplumber: PDF text extraction
• python-multipart: File upload support

Office Document Libraries

• python-docx: Word document processing
• openpyxl: Excel file processing
• python-pptx: PowerPoint presentation processing

Configuration

Environment Variables

• TESSERACT_CMD: Path to Tesseract executable (default: "tesseract")
• MAX_FILE_SIZE_MB: Maximum allowed file size in megabytes (default: "50")

Customizing File Size Limit

To change the maximum file size limit, modify the docker-compose.yml file:

environment:
  TESSERACT_CMD: "/usr/bin/tesseract"
  MAX_FILE_SIZE_MB: "100"  # Change to desired size in MB

Or set it when running manually:

export MAX_FILE_SIZE_MB=100
uvicorn app.main:app --host 0.0.0.0 --port 8000

Docker Configuration

The application is configured to run on port 8000. You can modify the port mapping in docker-compose.yml if needed.

Volume Mounts:

• ./app:/app/app - Application code
• ./static:/app/static - Web UI files

Error Handling

The API provides comprehensive error handling:

• 400 Bad Request: Invalid file format or corrupted files
• 413 Payload Too Large: File size exceeds the configured maximum limit
• 500 Internal Server Error: Processing errors with detailed error messages

The Web UI provides user-friendly error messages for all error scenarios.

Performance Considerations

• PDF Processing: Large PDFs with many images may take longer due to OCR processing
• Image Quality: Higher resolution images provide better OCR accuracy
• Memory Usage: Processing large files may require sufficient memory allocation

Development

Running in Development Mode

Terminal:

uvicorn app.main:app --reload --host 0.0.0.0 --port 8000

PyCharm:

• Use the run configuration setup described in the "Running from PyCharm" section
• Ensure your Python interpreter has all dependencies from requirements.txt installed
• The --reload flag enables auto-reload on code changes

Adding New File Formats

To add support for new file formats:

1. Install required libraries in requirements.txt
2. Add extraction logic in main.py
3. Update the API endpoint to handle the new format
4. Update this README with the new supported format

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

License

This project is part of the Berijalan Bootcamp Techno 14 program.

Troubleshooting

Common Issues

Tesseract not found:

• Ensure Tesseract is installed and accessible in your system PATH
• Set the TESSERACT_CMD environment variable to the correct path

Memory errors with large files:

• Increase Docker memory limits if using containers
• Consider implementing file size limits for your use case

Poor OCR accuracy:

• Ensure images have sufficient resolution (300+ DPI recommended)
• Preprocess images to improve contrast and clarity