# DocTR Tuning REST API

REST API service for DocTR (Document Text Recognition) hyperparameter evaluation. Keeps the model loaded in memory for fast repeated evaluations during hyperparameter search.

## Quick Start

### CPU Version

```bash
cd src/doctr_service

# Build
docker build -t doctr-api:cpu .

# Run
docker run -d -p 8003:8000 \
  -v $(pwd)/../dataset:/app/dataset:ro \
  -v doctr-cache:/root/.cache/doctr \
  doctr-api:cpu

# Test
curl http://localhost:8003/health
```

### GPU Version

```bash
# Build GPU image
docker build -f Dockerfile.gpu -t doctr-api:gpu .

# Run with GPU
docker run -d -p 8003:8000 --gpus all \
  -v $(pwd)/../dataset:/app/dataset:ro \
  -v doctr-cache:/root/.cache/doctr \
  doctr-api:gpu
```

## Files

| File | Description |
|------|-------------|
| `doctr_tuning_rest.py` | FastAPI REST service with 9 tunable hyperparameters |
| `dataset_manager.py` | Dataset loader (shared with other services) |
| `Dockerfile` | CPU-only image (amd64 + arm64) |
| `Dockerfile.gpu` | GPU/CUDA image (amd64 + arm64) |
| `requirements.txt` | Python dependencies |

## API Endpoints

### `GET /health`

Check if service is ready.

```json
{
  "status": "ok",
  "model_loaded": true,
  "dataset_loaded": true,
  "dataset_size": 24,
  "det_arch": "db_resnet50",
  "reco_arch": "crnn_vgg16_bn",
  "cuda_available": true,
  "device": "cuda",
  "gpu_name": "NVIDIA GB10"
}
```

### `POST /evaluate`

Run OCR evaluation with given hyperparameters.

**Request (9 tunable parameters):**
```json
{
  "pdf_folder": "/app/dataset",
  "assume_straight_pages": true,
  "straighten_pages": false,
  "preserve_aspect_ratio": true,
  "symmetric_pad": true,
  "disable_page_orientation": false,
  "disable_crop_orientation": false,
  "resolve_lines": true,
  "resolve_blocks": false,
  "paragraph_break": 0.035,
  "start_page": 5,
  "end_page": 10
}
```

**Response:**
```json
{
  "CER": 0.0189,
  "WER": 0.1023,
  "TIME": 52.3,
  "PAGES": 5,
  "TIME_PER_PAGE": 10.46,
  "model_reinitialized": false
}
```

**Note:** `model_reinitialized` indicates if the model was reloaded due to changed processing flags (adds ~2-5s overhead).

## Hyperparameters

### Processing Flags (Require Model Reinitialization)

| Parameter | Default | Description |
|-----------|---------|-------------|
| `assume_straight_pages` | true | Skip rotation handling for straight documents |
| `straighten_pages` | false | Pre-straighten pages before detection |
| `preserve_aspect_ratio` | true | Maintain document proportions during resize |
| `symmetric_pad` | true | Use symmetric padding when preserving aspect ratio |

**Note:** Changing these flags requires model reinitialization (~2-5s).

### Orientation Flags

| Parameter | Default | Description |
|-----------|---------|-------------|
| `disable_page_orientation` | false | Skip page orientation classification |
| `disable_crop_orientation` | false | Skip crop orientation detection |

### Output Grouping

| Parameter | Default | Range | Description |
|-----------|---------|-------|-------------|
| `resolve_lines` | true | bool | Group words into lines |
| `resolve_blocks` | false | bool | Group lines into blocks |
| `paragraph_break` | 0.035 | 0.0-1.0 | Minimum space ratio separating paragraphs |

## Model Architecture

DocTR uses a two-stage pipeline:

1. **Detection** (`det_arch`): Localizes text regions
   - Default: `db_resnet50` (DBNet with ResNet-50 backbone)
   - Alternatives: `linknet_resnet18`, `db_mobilenet_v3_large`

2. **Recognition** (`reco_arch`): Recognizes characters
   - Default: `crnn_vgg16_bn` (CRNN with VGG-16 backbone)
   - Alternatives: `sar_resnet31`, `master`, `vitstr_small`

Architecture is set via environment variables (fixed at startup).

## GPU Support

### Platform Support

| Platform | CPU | GPU |
|----------|-----|-----|
| Linux x86_64 (amd64) | ✅ | ✅ PyTorch CUDA |
| Linux ARM64 (GH200/GB200/DGX Spark) | ✅ | ✅ PyTorch CUDA (cu128 index) |
| macOS ARM64 (M1/M2) | ✅ | ❌ |

### PyTorch CUDA on ARM64

Unlike PaddlePaddle, PyTorch provides **official ARM64 CUDA wheels** on the cu128 index:

```bash
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu128
```

This works on both amd64 and arm64 platforms with CUDA support.

### GPU Detection

DocTR automatically uses GPU when available:

```python
import torch
print(torch.cuda.is_available())  # True if GPU available

# DocTR model moves to GPU
model = ocr_predictor(pretrained=True)
if torch.cuda.is_available():
    model = model.cuda()
```

The `/health` endpoint shows GPU status:
```json
{
  "cuda_available": true,
  "device": "cuda",
  "gpu_name": "NVIDIA GB10",
  "gpu_memory_total": "128.00 GB"
}
```

## Environment Variables

| Variable | Default | Description |
|----------|---------|-------------|
| `DOCTR_DET_ARCH` | `db_resnet50` | Detection architecture |
| `DOCTR_RECO_ARCH` | `crnn_vgg16_bn` | Recognition architecture |
| `CUDA_VISIBLE_DEVICES` | `0` | GPU device selection |

## CI/CD

Built images available from registry:

| Image | Architecture |
|-------|--------------|
| `seryus.ddns.net/unir/doctr-cpu:latest` | amd64, arm64 |
| `seryus.ddns.net/unir/doctr-gpu:latest` | amd64, arm64 |

## Sources

- [DocTR Documentation](https://mindee.github.io/doctr/)
- [DocTR GitHub](https://github.com/mindee/doctr)
- [DocTR Model Usage](https://mindee.github.io/doctr/latest/using_doctr/using_models.html)
- [PyTorch ARM64 CUDA Wheels](https://github.com/pytorch/pytorch/issues/160162)
