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---
tags:
- ocr
- document-processing
- nanonets
- nanonets-ocr2
- markdown
- uv-script
- generated
---
# Document OCR using Nanonets-OCR2-3B
This dataset contains markdown-formatted OCR results from images in [stckmn/ocr-input-Directive017-1761354068](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761354068) using Nanonets-OCR2-3B.
## Processing Details
- **Source Dataset**: [stckmn/ocr-input-Directive017-1761354068](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761354068)
- **Model**: [nanonets/Nanonets-OCR2-3B](https://huggingface.co/nanonets/Nanonets-OCR2-3B)
- **Model Size**: 3.75B parameters
- **Number of Samples**: 21
- **Processing Time**: 3.2 minutes
- **Processing Date**: 2025-10-25 01:05 UTC
### Configuration
- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 16
- **Max Model Length**: 8,192 tokens
- **Max Output Tokens**: 4,096
- **GPU Memory Utilization**: 80.0%
## Model Information
Nanonets-OCR2-3B is a state-of-the-art document OCR model that excels at:
- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format
- 📊 **Tables** - Extracted and formatted as HTML
- 📝 **Document structure** - Headers, lists, and formatting maintained
- 🖼️ **Images** - Captions and descriptions included in `<img>` tags
- ☑️ **Forms** - Checkboxes rendered as ☐/☑
- 🔖 **Watermarks** - Wrapped in `<watermark>` tags
- 📄 **Page numbers** - Wrapped in `<page_number>` tags
- 🌍 **Multilingual** - Supports multiple languages
## Dataset Structure
The dataset contains all original columns plus:
- `markdown`: The extracted text in markdown format with preserved structure
- `inference_info`: JSON list tracking all OCR models applied to this dataset
## Usage
```python
from datasets import load_dataset
import json
# Load the dataset
dataset = load_dataset("{{output_dataset_id}}", split="train")
# Access the markdown text
for example in dataset:
print(example["markdown"])
break
# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
print(f"Column: {{info['column_name']}} - Model: {{info['model_id']}}")
```
## Reproduction
This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) Nanonets OCR2 script:
```bash
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr2.py \
stckmn/ocr-input-Directive017-1761354068 \
<output-dataset> \
--model nanonets/Nanonets-OCR2-3B \
--image-column image \
--batch-size 16 \
--max-model-len 8192 \
--max-tokens 4096 \
--gpu-memory-utilization 0.8
```
## Performance
- **Processing Speed**: ~0.1 images/second
- **GPU Configuration**: vLLM with 80% GPU memory utilization
Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
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