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---
tags:
- ocr
- document-processing
- deepseek
- deepseek-ocr
- markdown
- uv-script
- generated
configs:
- config_name: glm-ocr
data_files:
- split: train
path: glm-ocr/train-*
dataset_info:
config_name: glm-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: 14621662.0
num_examples: 50
download_size: 14495272
dataset_size: 14621662.0
---
# Document OCR using DeepSeek-OCR
This dataset contains markdown-formatted OCR results from images in [biglam/rubenstein-manuscript-catalog](https://huggingface.co/datasets/biglam/rubenstein-manuscript-catalog) using DeepSeek-OCR.
## Processing Details
- **Source Dataset**: [biglam/rubenstein-manuscript-catalog](https://huggingface.co/datasets/biglam/rubenstein-manuscript-catalog)
- **Model**: [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR)
- **Number of Samples**: 50
- **Processing Time**: 5.6 min
- **Processing Date**: 2026-02-15 00:40 UTC
### Configuration
- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 8
- **Max Model Length**: 8,192 tokens
- **Max Output Tokens**: 8,192
- **GPU Memory Utilization**: 80.0%
## Model Information
DeepSeek-OCR 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/markdown
- Document structure - Headers, lists, and formatting maintained
- Image grounding - Spatial layout and bounding box information
- Complex layouts - Multi-column and hierarchical structures
- 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) DeepSeek OCR vLLM script:
```bash
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \\
biglam/rubenstein-manuscript-catalog \\
<output-dataset> \\
--image-column image
```
## Performance
- **Processing Speed**: ~0.1 images/second
- **Processing Method**: Batch processing with vLLM (2-3x speedup over sequential)
Generated with [UV Scripts](https://huggingface.co/uv-scripts)
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