328 lines
11 KiB
Markdown
328 lines
11 KiB
Markdown
# 🚀 DeepSeek OCR - React + FastAPI
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Modern OCR web application powered by DeepSeek-OCR with a stunning React frontend and FastAPI backend.
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> **Recent Updates (v2.1.1)**
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> - ✅ Fixed image removal button - now properly clears and allows re-upload
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> - ✅ Fixed multiple bounding boxes parsing - handles `[[x1,y1,x2,y2], [x1,y1,x2,y2]]` format
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> - ✅ Simplified to 4 core working modes for better stability
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> - ✅ Fixed bounding box coordinate scaling (normalized 0-999 → actual pixels)
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> - ✅ Fixed HTML rendering (model outputs HTML, not Markdown)
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> - ✅ Increased file upload limit to 100MB (configurable)
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> - ✅ Added .env configuration support
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## Quick Start
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1. **Clone and configure:**
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```bash
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git clone <repository-url>
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cd deepseek_ocr_app
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# Copy and customize environment variables
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cp .env.example .env
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# Edit .env to configure ports, upload limits, etc.
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```
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2. **Start the application:**
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```bash
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docker compose up --build
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```
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The first run will download the model (~5-10GB), which may take some time.
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3. **Access the application:**
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- **Frontend**: http://localhost:3000 (or your configured FRONTEND_PORT)
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- **Backend API**: http://localhost:8000 (or your configured API_PORT)
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- **API Docs**: http://localhost:8000/docs
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## Features
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### 4 Core OCR Modes
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- **Plain OCR** - Raw text extraction from any image
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- **Describe** - Generate intelligent image descriptions
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- **Find** - Locate specific terms with visual bounding boxes
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- **Freeform** - Custom prompts for specialized tasks
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### UI Features
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- 🎨 Glass morphism design with animated gradients
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- 🎯 Drag & drop file upload (up to 100MB by default)
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- 🗑️ Easy image removal and re-upload
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- 📦 Grounding box visualization with proper coordinate scaling
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- ✨ Smooth animations (Framer Motion)
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- 📋 Copy/Download results
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- 🎛️ Advanced settings dropdown
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- 📝 HTML and Markdown rendering for formatted output
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- 🔍 Multiple bounding box support (handles multiple instances of found terms)
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## Configuration
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The application can be configured via the `.env` file:
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```bash
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# API Configuration
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API_HOST=0.0.0.0
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API_PORT=8000
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# Frontend Configuration
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FRONTEND_PORT=3000
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# Model Configuration
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MODEL_NAME=deepseek-ai/DeepSeek-OCR
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HF_HOME=/models
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# Upload Configuration
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MAX_UPLOAD_SIZE_MB=100 # Maximum file upload size
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# Processing Configuration
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BASE_SIZE=1024 # Base processing resolution
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IMAGE_SIZE=640 # Tile processing resolution
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CROP_MODE=true # Enable dynamic cropping for large images
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```
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### Environment Variables
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- `API_HOST`: Backend API host (default: 0.0.0.0)
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- `API_PORT`: Backend API port (default: 8000)
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- `FRONTEND_PORT`: Frontend port (default: 3000)
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- `MODEL_NAME`: HuggingFace model identifier
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- `HF_HOME`: Model cache directory
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- `MAX_UPLOAD_SIZE_MB`: Maximum file upload size in megabytes
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- `BASE_SIZE`: Base image processing size (affects memory usage)
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- `IMAGE_SIZE`: Tile size for dynamic cropping
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- `CROP_MODE`: Enable/disable dynamic image cropping
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## Tech Stack
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- **Frontend**: React 18 + Vite 5 + TailwindCSS 3 + Framer Motion 11
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- **Backend**: FastAPI + PyTorch + Transformers 4.46 + DeepSeek-OCR
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- **Configuration**: python-decouple for environment management
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- **Server**: Nginx (reverse proxy)
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- **Container**: Docker + Docker Compose with multi-stage builds
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- **GPU**: NVIDIA CUDA support (tested on RTX 3090, RTX 5090)
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## Project Structure
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```
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deepseek-ocr/
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├── backend/ # FastAPI backend
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│ ├── main.py
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│ ├── requirements.txt
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│ └── Dockerfile
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├── frontend/ # React frontend
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│ ├── src/
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│ │ ├── components/
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│ │ ├── App.jsx
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│ │ └── main.jsx
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│ ├── package.json
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│ ├── nginx.conf
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│ └── Dockerfile
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├── models/ # Model cache
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└── docker-compose.yml
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```
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## Development
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Docker compose cycle to test:
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```bash
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docker compose down
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docker compose up --build
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```
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## Requirements
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### Hardware
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- NVIDIA GPU with CUDA support
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- Recommended: RTX 3090, RTX 4090, RTX 5090, or newer
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- Minimum: 8-12GB VRAM for the model
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- More VRAM allows for larger batch sizes and higher resolution images
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### Software
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- **Docker & Docker Compose** (latest version recommended)
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- **NVIDIA Driver** - Installing NVIDIA Drivers on Ubuntu (Blackwell/RTX 5090)
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**Note**: Getting NVIDIA drivers working on Blackwell GPUs can be a pain! Here's what worked:
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The key requirements for RTX 5090 on Ubuntu 24.04:
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- Use the open-source driver (nvidia-driver-570-open or newer, like nvidia-driver-580-open)
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- Upgrade to kernel 6.11+ (6.14+ recommended for best stability)
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- Enable Resize Bar in BIOS/UEFI (critical!)
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**Step-by-Step Instructions:**
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1. Install NVIDIA Open Driver (580 or newer)
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```bash
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sudo add-apt-repository ppa:graphics-drivers/ppa
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sudo apt update
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sudo apt remove --purge nvidia*
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sudo nvidia-installer --uninstall # If you have it
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sudo apt autoremove
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sudo apt install nvidia-driver-580-open
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```
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2. Upgrade Linux Kernel to 6.11+ (for Ubuntu 24.04 LTS)
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```bash
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sudo apt install --install-recommends linux-generic-hwe-24.04 linux-headers-generic-hwe-24.04
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sudo update-initramfs -u
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sudo apt autoremove
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```
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3. Reboot
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```bash
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sudo reboot
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```
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4. Enable Resize Bar in UEFI/BIOS
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- Restart and enter UEFI (usually F2, Del, or F12 during boot)
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- Find and enable "Resize Bar" or "Smart Access Memory"
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- This will also enable "Above 4G Decoding" and disable "CSM" (Compatibility Support Module)—that's expected!
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- Save and exit
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5. Verify Installation
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```bash
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nvidia-smi
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```
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You should see your RTX 5090 listed!
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💡 **Why open drivers?** I dunno, but the open drivers have better support for Blackwell GPUs. Without Resize Bar enabled, you'll get a black screen even with correct drivers!
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Credit: Solution adapted from [this Reddit thread](https://www.reddit.com/r/linux_gaming/comments/1i3h4gn/blackwell_on_linux/).
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- **NVIDIA Container Toolkit** (required for GPU access in Docker)
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- Installation guide: https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html
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### System Requirements
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- ~20GB free disk space (for model weights and Docker images)
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- 16GB+ system RAM recommended
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- Fast internet connection for initial model download (~5-10GB)
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## Known Issues & Fixes
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### ✅ FIXED: Image removal and re-upload (v2.1.1)
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- **Issue**: Couldn't remove uploaded image and upload a new one
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- **Fix**: Added prominent "Remove" button that clears image state and allows fresh upload
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### ✅ FIXED: Multiple bounding boxes (v2.1.1)
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- **Issue**: Only single bounding box worked, multiple boxes like `[[x1,y1,x2,y2], [x1,y1,x2,y2]]` failed
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- **Fix**: Updated parser to handle both single and array of coordinate arrays using `ast.literal_eval`
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### ✅ FIXED: Grounding box coordinate scaling (v2.1)
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- **Issue**: Bounding boxes weren't displaying correctly
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- **Cause**: Model outputs coordinates normalized to 0-999, not actual pixel dimensions
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- **Fix**: Backend now properly scales coordinates using the formula: `actual_coord = (normalized_coord / 999) * image_dimension`
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### ✅ FIXED: HTML vs Markdown rendering (v2.1)
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- **Issue**: Output was being rendered as Markdown when model outputs HTML
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- **Cause**: Model is trained to output HTML (especially for tables)
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- **Fix**: Frontend now detects and renders HTML properly using `dangerouslySetInnerHTML`
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### ✅ FIXED: Limited upload size (v2.1)
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- **Issue**: Large images couldn't be uploaded
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- **Fix**: Increased nginx `client_max_body_size` to 100MB (configurable via .env)
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### ⚠️ Simplified Mode Selection (v2.1.1)
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- **Change**: Reduced from 12 modes to 4 core working modes
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- **Reason**: Advanced modes (tables, layout, PII, multilingual) need additional testing
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- **Working modes**: Plain OCR, Describe, Find, Freeform
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- **Future**: Additional modes will be re-enabled after thorough testing
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## How the Model Works
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### Coordinate System
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The DeepSeek-OCR model uses a normalized coordinate system (0-999) for bounding boxes:
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- All coordinates are output in range [0, 999]
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- Backend scales: `pixel_coord = (model_coord / 999) * actual_dimension`
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- This ensures consistency across different image sizes
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### Dynamic Cropping
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For large images, the model uses dynamic cropping:
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- Images ≤640x640: Direct processing
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- Larger images: Split into tiles based on aspect ratio
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- Global view (BASE_SIZE) + Local views (IMAGE_SIZE tiles)
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- See `process/image_process.py` for implementation details
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### Output Format
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- Plain text modes: Return raw text
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- Table modes: Return HTML tables or CSV
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- JSON modes: Return structured JSON
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- Grounding modes: Return text with `<|ref|>label<|/ref|><|det|>[[coords]]<|/det|>` tags
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## API Usage
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### POST /api/ocr
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**Parameters:**
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- `image` (file, required) - Image file to process (up to 100MB)
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- `mode` (string) - OCR mode: `plain_ocr` | `describe` | `find_ref` | `freeform`
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- `prompt` (string) - Custom prompt for freeform mode
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- `grounding` (bool) - Enable bounding boxes (auto-enabled for find_ref)
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- `find_term` (string) - Term to locate in find_ref mode (supports multiple matches)
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- `base_size` (int) - Base processing size (default: 1024)
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- `image_size` (int) - Tile size for cropping (default: 640)
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- `crop_mode` (bool) - Enable dynamic cropping (default: true)
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- `include_caption` (bool) - Add image description (default: false)
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**Response:**
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```json
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{
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"success": true,
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"text": "Extracted text or HTML output...",
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"boxes": [{"label": "field", "box": [x1, y1, x2, y2]}],
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"image_dims": {"w": 1920, "h": 1080},
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"metadata": {
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"mode": "layout_map",
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"grounding": true,
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"base_size": 1024,
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"image_size": 640,
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"crop_mode": true
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}
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}
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```
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**Note on Bounding Boxes:**
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- The model outputs coordinates normalized to 0-999
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- The backend automatically scales them to actual image dimensions
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- Coordinates are in [x1, y1, x2, y2] format (top-left, bottom-right)
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- **Supports multiple boxes**: When finding multiple instances, format is `[[x1,y1,x2,y2], [x1,y1,x2,y2], ...]`
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- Frontend automatically displays all boxes overlaid on the image with unique colors
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## Examples
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Here are some example images showcasing different OCR capabilities:
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### Visual Understanding
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### Table Extraction from Chart
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### Image Description
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## Troubleshooting
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### GPU not detected
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```bash
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nvidia-smi
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docker run --rm --gpus all nvidia/cuda:11.8.0-base-ubuntu22.04 nvidia-smi
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```
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### Port conflicts
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```bash
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sudo lsof -i :3000
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sudo lsof -i :8000
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```
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### Frontend build issues
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```bash
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cd frontend
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rm -rf node_modules package-lock.json
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docker-compose build frontend
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```
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## License
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This project uses the DeepSeek-OCR model. Refer to the model's license terms.
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