README.md

---
title: QuickPDF OCR
emoji: 📄
colorFrom: blue
colorTo: blue
sdk: docker
app_port: 8000
pinned: false
---

# QuickPDF Studio: OCR Microservice

A dedicated, self-hosted OCR extraction API built with **FastAPI**, **EasyOCR**, and **PyMuPDF**.

## Features
- **Pure REST API**: No frontend bloat, just high-performance endpoints.
- **Multi-Format Support**: JPG, PNG, WebP, and multi-page scanned PDFs.
- **Localized**: Pre-configured for English ('en') and German ('de').
- **Privacy First**: Files are processed in memory and never persisted to disk.
- **Docker Ready**: Easy deployment on any system.

## Quick Start (Local)

1. **Install Dependencies**:
   ```bash
   pip install -r requirements.txt
   ```

2. **Run Server**:
   ```bash
   uvicorn main:app --reload
   ```

3. **Check Health**:
   ```bash
   curl http://localhost:8000/health
   ```

## API Usage

### `POST /ocr`
Extract text from a document.

**Parameters**:
- `file`: Multipart/form-data upload.
- `languages` (Optional): Query param (e.g. `?languages=en,de`).

**Example Request (Curl)**:
```bash
curl -X 'POST' \
  'http://localhost:8000/ocr?languages=en,de' \
  -H 'accept: application/json' \
  -H 'Content-Type: multipart/form-data' \
  -F 'file=@sample.pdf'
```

**Example Response**:
```json
{
  "success": true,
  "text": "Full extracted text...",
  "pages": [
    {"page": 1, "text": "Page 1 text..."}
  ],
  "language_used": ["en", "de"]
}
```

## Docker Deployment

1. **Build Image**:
   ```bash
   docker build -t quickpdf-ocr .
   ```

2. **Run Container**:
   ```bash
   docker run -d -p 8000:8000 --name ocr-service quickpdf-ocr
   ```

## Notes
- **Models**: On first run, EasyOCR will download approximately 150MB of trained models to `~/.EasyOCR`.
- **Resources**: This service is optimized for CPU. It is recommended to have at least 2GB of RAM available.