# OCR Scripts - Development Notes

## Active Scripts

### DeepSeek-OCR v1 (`deepseek-ocr-vllm.py`)
✅ **Production Ready**
- Fully supported by vLLM
- Fast batch processing
- Tested and working on HF Jobs

### LightOnOCR-2-1B (`lighton-ocr2.py`)
⚠️ **Temporarily Broken** (2026-01-29)

**Status:** vLLM nightly regression - image processor loading fails

**What happened:**
- Script was working with vLLM nightly `v0.15.0rc2.dev73`
- Nightly updated to `v0.15.0rc2.dev81` and broke
- Error: `OSError: Can't load image processor for 'lightonai/LightOnOCR-2-1B'`
- Both nightly and stable vLLM 0.14.x have this issue now

**Initial test results (before breakage):**
- 8/10 samples had good OCR output
- 2/10 samples showed repetition loops (likely due to max_tokens=6144)
- Changed max_tokens default from 6144 → 4096 (per model card recommendation)

**Fixes applied:**
- `max_tokens`: 6144 → 4096 (model card recommends 4096 for arXiv papers)
- Fixed pyarrow compatibility (>=17.0.0,<18.0.0)
- Replaced deprecated `huggingface-hub[hf_transfer]` with `hf-xet`

**To verify when vLLM is fixed:**
```bash
hf jobs uv run --flavor a100-large \
    -s HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
    davanstrien/ufo-ColPali davanstrien/lighton-ocr2-test-v3 \
    --max-samples 10 --shuffle --seed 42
```

**Model Info:**
- Model: `lightonai/LightOnOCR-2-1B`
- Architecture: Pixtral ViT encoder + Qwen3 LLM
- Training: RLVR (Reinforcement Learning with Verifiable Rewards)
- Performance: 83.2% on OlmOCR-Bench, 42.8 pages/sec on H100

## Pending Development

### DeepSeek-OCR-2 (Visual Causal Flow Architecture)

**Status:** ⏳ Waiting for vLLM upstream support

**Context:**
DeepSeek-OCR-2 is the next generation OCR model (3B parameters) with Visual Causal Flow architecture offering improved quality. We attempted to create a UV script (`deepseek-ocr2-vllm.py`) but encountered a blocker.

**Blocker:**
vLLM does not yet support `DeepseekOCR2ForCausalLM` architecture in the official release.

**PR to Watch:**
🔗 https://github.com/vllm-project/vllm/pull/33165

This PR adds DeepSeek-OCR-2 support but is currently:
- ⚠️ **Open** (not merged)
- Has unresolved review comments
- Pre-commit checks failing
- Issues: hardcoded parameters, device mismatch bugs, missing error handling

**What's Needed:**
1. PR #33165 needs to be reviewed, fixed, and merged
2. vLLM needs to release a version including the merge
3. Then we can add these dependencies to our script:
   ```python
   # dependencies = [
   #     "datasets>=4.0.0",
   #     "huggingface-hub",
   #     "pillow",
   #     "vllm",
   #     "tqdm",
   #     "toolz",
   #     "torch",
   #     "addict",
   #     "matplotlib",
   # ]
   ```

**Implementation Progress:**
- ✅ Created `deepseek-ocr2-vllm.py` script
- ✅ Fixed dependency issues (pyarrow, datasets>=4.0.0)
- ✅ Tested script structure on HF Jobs
- ❌ Blocked: vLLM doesn't recognize architecture

**Partial Implementation:**
The file `deepseek-ocr2-vllm.py` exists in this repo but is **not functional** until vLLM support lands. Consider it a draft.

**Testing Evidence:**
When we ran on HF Jobs, we got:
```
ValidationError: Model architectures ['DeepseekOCR2ForCausalLM'] are not supported for now.
Supported architectures: [...'DeepseekOCRForCausalLM'...]
```

**Next Steps (when PR merges):**
1. Update `deepseek-ocr2-vllm.py` dependencies to include `addict` and `matplotlib`
2. Test on HF Jobs with small dataset (10 samples)
3. Verify output quality
4. Update README.md with DeepSeek-OCR-2 section
5. Document v1 vs v2 differences

**Alternative Approaches (if urgent):**
- Create transformers-based script (slower, no vLLM batching)
- Use DeepSeek's official repo setup (complex, not UV-script compatible)

**Model Information:**
- Model ID: `deepseek-ai/DeepSeek-OCR-2`
- Model Card: https://huggingface.co/deepseek-ai/DeepSeek-OCR-2
- GitHub: https://github.com/deepseek-ai/DeepSeek-OCR-2
- Parameters: 3B
- Resolution: (0-6)×768×768 + 1×1024×1024 patches
- Key improvement: Visual Causal Flow architecture

**Resolution Modes (for v2):**
```python
RESOLUTION_MODES = {
    "tiny": {"base_size": 512, "image_size": 512, "crop_mode": False},
    "small": {"base_size": 640, "image_size": 640, "crop_mode": False},
    "base": {"base_size": 1024, "image_size": 768, "crop_mode": False},  # v2 optimized
    "large": {"base_size": 1280, "image_size": 1024, "crop_mode": False},
    "gundam": {"base_size": 1024, "image_size": 768, "crop_mode": True},  # v2 optimized
}
```

## Other OCR Scripts

### Nanonets OCR (`nanonets-ocr.py`, `nanonets-ocr2.py`)
✅ Both versions working

### PaddleOCR-VL (`paddleocr-vl.py`)
✅ Working

---

**Last Updated:** 2026-01-29
**Watch PRs:**
- DeepSeek-OCR-2: https://github.com/vllm-project/vllm/pull/33165
- LightOnOCR-2 regression: Check https://github.com/vllm-project/vllm/issues?q=LightOnOCR