File size: 2,603 Bytes
55e4256 2ec8015 55e4256 2ec8015 55e4256 2ec8015 55e4256 2ec8015 55e4256 2ec8015 55e4256 2ec8015 55e4256 17b958c 55e4256 2ec8015 55e4256 2ec8015 55e4256 4b5f951 2ec8015 4b5f951 2ec8015 4b5f951 55e4256 2ec8015 4b5f951 55e4256 2ec8015 55e4256 2ec8015 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 |
---
tags:
- ocr
- document-processing
- dots-ocr
- multilingual
- markdown
- uv-script
- generated
---
# Document OCR using dots.ocr
This dataset contains OCR results from images in [NationalLibraryOfScotland/medical-history-of-british-india](https://huggingface.co/datasets/NationalLibraryOfScotland/medical-history-of-british-india) using DoTS.ocr, a compact 1.7B multilingual model.
## Processing Details
- **Source Dataset**: [NationalLibraryOfScotland/medical-history-of-british-india](https://huggingface.co/datasets/NationalLibraryOfScotland/medical-history-of-british-india)
- **Model**: [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr)
- **Number of Samples**: 10
- **Processing Time**: 6.2 min
- **Processing Date**: 2026-02-14 18:47 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 \
NationalLibraryOfScotland/medical-history-of-british-india \
<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)
|