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| # Zim Docs OCR-to-JSON Extractor | |
| ## Overview | |
| Welcome to the **Zim Docs OCR-to-JSON Extractor**! This is a powerful and user-friendly web application built with Gradio, designed to help you upload scanned documents (PDFs) or images (PNG, JPG, etc.). It then uses a vision AI model to perform Optical Character Recognition (OCR) and extract structured information into a JSON format. This tool aims to streamline your process of digitizing and organizing data from various document types, such as **driver's licenses, passports, national ID cards, invoices, receipts, and more.** | |
| ## Requirements | |
| To use this application, you'll need: | |
| * Python 3.7+ | |
| * Gradio | |
| * Gradio-PDF (`gradio_pdf`) | |
| * Requests | |
| * PyMuPDF (`fitz`) | |
| * An API Key from [OpenRouter.ai](https://openrouter.ai/) (or any other service compatible with the OpenAI chat completions API format). | |
| * You should set this key as an environment variable named `API_KEY`. The Python script uses `os.getenv("API_KEY")` to retrieve this key. If you're using Hugging Face Spaces, you can set this as a "Secret". | |
| ## Running the Application | |
| **On Hugging Face Spaces:** | |
| This application is designed for deployment on Hugging Face Spaces. | |
| 1. Ensure your `requirements.txt` file in your Hugging Face Space repository lists all necessary dependencies (e.g., `gradio`, `gradio_pdf`, `requests`, `PyMuPDF`). | |
| 2. You should configure your `API_KEY` as a "Secret" in your Hugging Face Space settings. The application will then retrieve it using `os.getenv("API_KEY")`. | |
| 3. Once deployed, you can access the application via the URL provided by your Hugging Face Space. | |
| * **Live Demo:** You can try out a live demo of this application at: [Demo](https://huggingface.co/spaces/NyashaK/DocOCR2JSON) | |
| **For Local Development/Testing (Optional):** | |
| If you wish to run the application on your local machine: | |
| 1. Make sure you have all dependencies listed under "Requirements" installed in your local Python environment (e.g., by running `pip install gradio gradio_pdf requests PyMuPDF`). | |
| 2. Set the `API_KEY` environment variable on your local system. | |
| 3. You can then run the application using the command: | |
| ```bash | |
| python app.py | |
| ``` | |
| Replace `app.py` with the actual name of your Python file. It will typically be available at `http://127.0.0.1:7860`. | |
| ## How to Use | |
| 1. **Access the Application:** Open the URL of your Hugging Face Space where the application is deployed (see Live Demo link above), or your local URL if running it locally. | |
| 2. **Upload Your Document:** | |
| * Drag and drop a supported file (PDF, PNG, JPG, etc.) into the designated upload area. | |
| * Alternatively, click on the upload area to open your file browser and select the document. | |
| 3. **View Preview:** | |
| * Once you've uploaded a file, the "Document Preview" tab will attempt to display the image or the first page of your PDF. | |
| 4. **Check Extracted Data:** | |
| * The application will automatically process your document. | |
| * Switch to the "Extracted Data (JSON)" tab to view the structured information extracted by the AI model. | |
| * If any errors occur during processing (e.g., unsupported file type, API issue), an error message will be displayed in the JSON output area. | |
| --- | |
| title: OCRDocs2JSON | |
| emoji: 🧾 | |
| colorFrom: indigo | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: "4.26.0" | |
| app_file: app.py | |
| pinned: false | |
| --- | |