# 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`)
✅ **Production Ready** (Fixed 2026-01-29)

**Status:** Working with vLLM nightly

**What was fixed:**
- Root cause was NOT vLLM - it was the deprecated `HF_HUB_ENABLE_HF_TRANSFER=1` env var
- The script was setting this env var but `hf_transfer` package no longer exists
- This caused download failures that manifested as "Can't load image processor" errors
- Fix: Removed the `HF_HUB_ENABLE_HF_TRANSFER=1` setting from the script

**Test results (2026-01-29):**
- 10/10 samples processed successfully
- Clean markdown output with proper headers and paragraphs
- Output dataset: `davanstrien/lighton-ocr2-test-v4`

**Example usage:**
```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 output-dataset \
    --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

### PaddleOCR-VL-1.5 (`paddleocr-vl-1.5.py`)
✅ **Production Ready** (Added 2026-01-30)

**Status:** Working with transformers

**Note:** Uses transformers backend (not vLLM) because PaddleOCR-VL only supports vLLM in server mode, which doesn't fit the single-command UV script pattern. Images are processed one at a time for stability.

**Test results (2026-01-30):**
- 10/10 samples processed successfully
- Processing time: ~50s per image on L4 GPU
- Output dataset: `davanstrien/paddleocr-vl15-final-test`

**Example usage:**
```bash
hf jobs uv run --flavor l4x1 \
    -s HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \
    davanstrien/ufo-ColPali output-dataset \
    --max-samples 10 --shuffle --seed 42
```

**Task modes:**
- `ocr` (default): General text extraction to markdown
- `table`: Table extraction to HTML format
- `formula`: Mathematical formula recognition to LaTeX
- `chart`: Chart and diagram analysis
- `spotting`: Text spotting with localization (uses higher resolution)
- `seal`: Seal and stamp recognition

**Model Info:**
- Model: `PaddlePaddle/PaddleOCR-VL-1.5`
- Size: 0.9B parameters (ultra-compact)
- Performance: 94.5% SOTA on OmniDocBench v1.5
- Backend: Transformers (single image processing)
- Requires: `transformers>=5.0.0`

## 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

---

## Future: OCR Smoke Test Dataset

**Status:** Idea (noted 2026-02-12)

Build a small curated dataset (`uv-scripts/ocr-smoke-test`?) with ~2-5 samples from diverse sources. Purpose: fast CI-style verification that scripts still work after dep updates, without downloading full datasets.

**Design goals:**
- Tiny (~20-30 images total) so download is seconds not minutes
- Covers the axes that break things: document type, image quality, language, layout complexity
- Has ground truth text where possible for quality regression checks
- All permissively licensed (CC0/CC-BY preferred)

**Candidate sources:**

| Source | What it covers | Why |
|--------|---------------|-----|
| `NationalLibraryOfScotland/medical-history-of-british-india` | Historical English, degraded scans | Has hand-corrected `text` column for comparison. CC0. Already tested with GLM-OCR. |
| `davanstrien/ufo-ColPali` | Mixed modern documents | Already used as our go-to test set. Varied layouts. |
| Something with **tables** | Structured data extraction | Tests `--task table` modes. Maybe a financial report or census page. |
| Something with **formulas/LaTeX** | Math notation | Tests `--task formula`. arXiv pages or textbook scans. |
| Something **multilingual** (CJK, Arabic, etc.) | Non-Latin scripts | GLM-OCR claims zh/ja/ko support. Good to verify. |
| Something **handwritten** | Handwriting recognition | Edge case that reveals model limits. |

**How it would work:**
```bash
# Quick smoke test for any script
uv run glm-ocr.py uv-scripts/ocr-smoke-test smoke-out --max-samples 5
# Or a dedicated test runner that checks all scripts against it
```

**Open questions:**
- Build as a proper HF dataset, or just a folder of images in the repo?
- Should we include expected output for regression testing (fragile if models change)?
- Could we add a `--smoke-test` flag to each script that auto-uses this dataset?
- Worth adding to HF Jobs scheduled runs for ongoing monitoring?

---

**Last Updated:** 2026-02-12
**Watch PRs:**
- DeepSeek-OCR-2: https://github.com/vllm-project/vllm/pull/33165