This repository contains a Flask-based REST API for performing Optical Character Recognition (OCR) on uploaded images. It utilizes the Flask-RESTx library to define the API endpoints and manage request and response formats.
- Python (>=3.6)
- Install required packages using
pip install -r requirements.txt
- Clone this repository:
git clone https://github.com/idan1356/flask_OCR_Service.git - Navigate to the project directory:
cd ocr-service - Run the application:
python app.py
The API will be accessible at http://localhost:5000/api/decipher-text.
The API documentation provides detailed information about the available endpoints, request and response formats, and example usage. You can access the documentation by visiting /api/docs in your browser.
Endpoint: POST /api/decipher-text
This endpoint allows you to upload an image for Optical Character Recognition.
Parameters:
user_image: The image file to be processed.langs: Comma-separated list of languages in the image (see language list in config file).get_processed_image: Whether the processed image (rectangles around sentences found by the model) should be returned.
Responses:
200 OK: OCR successful. Returns OCR results and optional processed image.400 Bad Request: OCR unsuccessful. Returns an error message.
The project is organized as follows:
app.py: The main Flask application file.api/: Contains the API definition and resources.api/components/: Contains parser and data model definitions.api/utils/: Contains utility functions for processing images and handling API logic.
The OCR model used by the service is LRU (Least Recently Used) cached to improve performance. The caching is implemented using the functools.lru_cache decorator, to cache OCR models based on the languages used for processing.
You can configure languages and other settings in the config file.
Contributions are welcome! Please open issues or submit pull requests for bug fixes, features, or improvements.
This project is licensed under the MIT License.