README.md

---
tags:
- ocr
- document-processing
- dots-ocr
- multilingual
- markdown
- uv-script
- generated
configs:
- config_name: deepseek-ocr
  data_files:
  - split: train
    path: deepseek-ocr/train-*
dataset_info:
  config_name: deepseek-ocr
  features:
  - name: image
    dtype: image
  - name: drawer_id
    dtype: string
  - name: card_number
    dtype: int64
  - name: filename
    dtype: string
  - name: text
    dtype: string
  - name: has_ocr
    dtype: bool
  - name: source
    dtype: string
  - name: source_url
    dtype: string
  - name: ia_collection
    dtype: string
  - name: markdown
    dtype: string
  - name: inference_info
    dtype: string
  splits:
  - name: train
    num_bytes: 14636668
    num_examples: 50
  download_size: 14453748
  dataset_size: 14636668
---

# 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 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**: [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr)
- **Number of Samples**: 50
- **Processing Time**: 5.3 min
- **Processing Date**: 2026-02-15 00:39 UTC

### Configuration

- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 16
- **Prompt Mode**: ocr
- **Max Model Length**: 8,192 tokens
- **Max Output Tokens**: 8,192
- **GPU Memory Utilization**: 80.0%

## Model Information

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
- `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) DoTS OCR script:

```bash
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 \
    --prompt-mode ocr \
    --max-model-len 8192 \
    --max-tokens 8192 \
    --gpu-memory-utilization 0.8
```

Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)