Image Processing API

A Flask-based REST API that extracts visual data from images including text recognition (OCR), metadata extraction, and color analysis.

Features

• Text Extraction (OCR): Extract text from images using Tesseract OCR
• Image Metadata: Extract EXIF data, dimensions, format, and file information
• Color Analysis: Identify dominant colors and calculate color statistics
• Multiple Format Support: PNG, JPG, JPEG, GIF, BMP, TIFF, WebP
• Base64 Encoding: Optional return of processed image as base64

Prerequisites

Before running this application, you need to install Tesseract OCR on your system:

Windows

1. Download Tesseract installer from: https://github.com/UB-Mannheim/tesseract/wiki
2. Install Tesseract (note the installation path)
3. Add Tesseract to your system PATH or set the path in code:

   pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'

macOS

brew install tesseract

Linux (Ubuntu/Debian)

sudo apt-get update
sudo apt-get install tesseract-ocr

Installation

1. Clone or download this project

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • Windows: venv\Scripts\activate
   • macOS/Linux: source venv/bin/activate

4. Install dependencies:

   pip install -r requirements.txt

Running the API

python main.py

The API will start on http://localhost:5000

API Endpoints

1. Health Check

• URL: GET /
• Description: Check if the API is running
• Response: JSON with API status and available endpoints

2. Extract Image Data

• URL: POST /extract
• Description: Upload an image and extract visual data
• Request: Multipart form data with image field
• Response: JSON with extracted data

Usage Examples

Using curl

# Upload an image for processing
curl -X POST -F "image=@path/to/your/image.jpg" http://localhost:5000/extract

Using Python requests

import requests

url = "http://localhost:5000/extract"
files = {"image": open("path/to/your/image.jpg", "rb")}
response = requests.post(url, files=files)
data = response.json()
print(data)

Using JavaScript/Fetch

const formData = new FormData();
formData.append('image', fileInput.files[0]);

fetch('http://localhost:5000/extract', {
    method: 'POST',
    body: formData
})
.then(response => response.json())
.then(data => console.log(data));

Response Format

The API returns a JSON response with the following structure:

{
    "success": true,
    "timestamp": "2024-01-15T10:30:00.000Z",
    "original_filename": "document.jpg",
    "file_size": 1048576,
    "extracted_text": {
        "raw_text": "Extracted text from the image...",
        "detailed_text": [
            {
                "text": "Hello",
                "confidence": 95,
                "bbox": {"x": 100, "y": 50, "width": 80, "height": 25}
            }
        ],
        "success": true
    },
    "metadata": {
        "format": "JPEG",
        "mode": "RGB",
        "size": {"width": 1920, "height": 1080},
        "has_transparency": false,
        "exif": {...},
        "file_size": 1048576
    },
    "color_analysis": {
        "mean_color": {"r": 128, "g": 64, "b": 192},
        "dominant_colors": [
            {
                "color": {"r": 255, "g": 255, "b": 255},
                "hex": "#ffffff",
                "percentage": 45.6
            }
        ]
    },
    "image_base64": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQ..."
}

Configuration

You can modify these settings in main.py:

• MAX_FILE_SIZE: Maximum upload file size (default: 16MB)
• ALLOWED_EXTENSIONS: Supported file formats
• UPLOAD_FOLDER: Temporary upload directory
• OCR confidence threshold (default: 30)

Error Handling

The API handles various error scenarios:

• 400: Bad request (no file, invalid format, etc.)
• 413: File too large
• 500: Internal server error

File Structure

.
├── main.py              # Main Flask application
├── requirements.txt     # Python dependencies
├── README.md           # This file
└── uploads/            # Temporary upload directory (created automatically)

Security Notes

• Files are temporarily stored and automatically deleted after processing
• CORS is enabled for cross-origin requests
• File size limits are enforced
• Only specific image formats are allowed

Troubleshooting

1. Tesseract not found: Make sure Tesseract is installed and in your PATH
2. Memory errors: Reduce image size or increase system memory
3. Permission errors: Ensure the application has write permissions for the uploads folder

License

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