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README.md

@@ -2,22 +2,23 @@
 tags:
 - ocr
 - document-processing
-- lighton-ocr-2
+- dots-ocr
+- multilingual
 - markdown
 - uv-script
 - generated
 ---
 
-# Document OCR using LightOnOCR-2-1B
+# Document OCR using dots.ocr
 
-This dataset contains OCR results from images in [biglam/rubenstein-manuscript-catalog](https://huggingface.co/datasets/biglam/rubenstein-manuscript-catalog) using LightOnOCR-2, a fast and compact 1B OCR model trained with RLVR.
+This dataset contains OCR results from images in [biglam/rubenstein-manuscript-catalog](https://huggingface.co/datasets/biglam/rubenstein-manuscript-catalog) using DoTS.ocr, a compact 1.7B multilingual model.
 
 ## Processing Details
 
 - **Source Dataset**: [biglam/rubenstein-manuscript-catalog](https://huggingface.co/datasets/biglam/rubenstein-manuscript-catalog)
-- **Model**: [lightonai/LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B)
+- **Model**: [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr)
 - **Number of Samples**: 50
-- **Processing Time**: 6.4 min
+- **Processing Time**: 5.3 min
 - **Processing Date**: 2026-02-15 00:39 UTC
 
 ### Configuration
@@ -26,38 +27,24 @@ This dataset contains OCR results from images in [biglam/rubenstein-manuscript-c
 - **Output Column**: `markdown`
 - **Dataset Split**: `train`
 - **Batch Size**: 16
-- **Target Image Size**: 1540px (longest dimension)
+- **Prompt Mode**: ocr
 - **Max Model Length**: 8,192 tokens
-- **Max Output Tokens**: 4,096
-- **Temperature**: 0.2
-- **Top P**: 0.9
+- **Max Output Tokens**: 8,192
 - **GPU Memory Utilization**: 80.0%
 
 ## Model Information
 
-LightOnOCR-2 is a next-generation fast, compact OCR model that excels at:
-- ⚡ **Fastest Speed** - 42.8 pages/second on H100 GPU (7× faster than v1)
-- 🎯 **High Accuracy** - 83.2 ± 0.9% on OlmOCR-Bench (+7.1% vs v1)
-- 🧠 **RLVR Training** - Eliminates repetition loops and formatting errors
-- 📚 **Better Dataset** - 2.5× larger training data with cleaner annotations
-- 📐 **LaTeX formulas** - Mathematical notation in LaTeX format
-- 📊 **Tables** - Extracted and formatted as markdown
-- 📝 **Document structure** - Hierarchy and layout preservation
-- 🌍 **Multilingual** - Optimized for European languages
-- 💪 **Production-ready** - Outperforms models 9× larger
-
-### Key Improvements over v1
-
-- **7.5× faster**: 42.8 vs 5.71 pages/sec on H100
-- **+7.1% accuracy**: 83.2% vs 76.1% on benchmarks
-- **Better quality**: RLVR training eliminates common OCR errors
-- **Cleaner output**: No repetition loops or formatting glitches
-- **Simpler**: Single model (no vocabulary variants)
+DoTS.ocr is a compact multilingual document parsing model that excels at:
+- 🌍 **100+ Languages** - Multilingual document support
+- 📊 **Table extraction** - Structured data recognition
+- 📐 **Formulas** - Mathematical notation preservation
+- 📝 **Layout-aware** - Reading order and structure preservation
+- 🎯 **Compact** - Only 1.7B parameters
 
 ## Dataset Structure
 
 The dataset contains all original columns plus:
-- `markdown`: The extracted text in markdown format with LaTeX formulas
+- `markdown`: The extracted text in markdown format
 - `inference_info`: JSON list tracking all OCR models applied to this dataset
 
 ## Usage
@@ -82,20 +69,18 @@ for info in inference_info:
 
 ## Reproduction
 
-This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) LightOnOCR-2 script:
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) DoTS OCR script:
 
 ```bash
-uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
     biglam/rubenstein-manuscript-catalog \
     <output-dataset> \
     --image-column image \
-    --batch-size 16
+    --batch-size 16 \
+    --prompt-mode ocr \
+    --max-model-len 8192 \
+    --max-tokens 8192 \
+    --gpu-memory-utilization 0.8
 ```
 
-## Performance
-
-- **Processing Speed**: ~0.13 images/second
-- **Benchmark Score**: 83.2 ± 0.9% on OlmOCR-Bench
-- **Training**: RLVR (Reinforcement Learning with Verifiable Rewards)
-
 Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)