Add LightOnOCR documentation to README

Add LightOnOCR as first script in README with recommendation to test
first since it's small and fast.

Features highlighted:
- Fastest OCR option (5.71 pages/sec on H100, 6.25 images/sec on A100)
- Compact 1B parameters for quick downloads
- 3 vocabulary sizes (151k/32k/16k) with European language optimization
- LaTeX formulas and table extraction
- Production-ready with 76.1% benchmark score

Includes quick start examples:
- L4 test run with 100 samples
- A100 production run with batch_size=4096

Positioning LightOnOCR as the recommended starting point for users
new to the OCR scripts due to fast initialization and excellent
speed/quality balance.

🤖 Generated with Claude Code

Co-Authored-By: Claude <noreply@anthropic.com>

README.md

@@ -31,6 +31,44 @@ That's it! The script will:
 
 ## 📋 Available Scripts
 
+### LightOnOCR (`lighton-ocr.py`) ⚡ Good one to test first since it's small and fast!
+
+Fast and compact OCR using [lightonai/LightOnOCR-1B-1025](https://huggingface.co/lightonai/LightOnOCR-1B-1025):
+
+- ⚡ **Fastest**: 5.71 pages/sec on H100, ~6.25 images/sec on A100 with batch_size=4096
+- 🎯 **Compact**: Only 1B parameters - quick to download and initialize
+- 🌍 **Multilingual**: 3 vocabulary sizes for different use cases
+- 📐 **LaTeX formulas**: Mathematical notation in LaTeX format
+- 📊 **Table extraction**: Markdown table format
+- 📝 **Document structure**: Preserves hierarchy and layout
+- 🚀 **Production-ready**: 76.1% benchmark score, used in production
+
+**Vocabulary sizes:**
+- `151k`: Full vocabulary, all languages (default)
+- `32k`: European languages, ~12% faster decoding
+- `16k`: European languages, ~12% faster decoding
+
+**Quick start:**
+```bash
+# Test on 100 samples with English text (32k vocab is fastest for European languages)
+hf jobs uv run --flavor l4x1 \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \
+    your-input-dataset your-output-dataset \
+    --vocab-size 32k \
+    --batch-size 32 \
+    --max-samples 100
+
+# Full production run on A100 (can handle huge batches!)
+hf jobs uv run --flavor a100-large \
+    -s HF_TOKEN \
+    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \
+    your-input-dataset your-output-dataset \
+    --vocab-size 32k \
+    --batch-size 4096 \
+    --temperature 0.0
+```
+
 ### DeepSeek-OCR (`deepseek-ocr-vllm.py`) ⭐ NEW
 
 Advanced document OCR using [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) with visual-text compression: