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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: text
dtype: string
- name: image_name
dtype: string
- name: type
dtype: string
- name: source_dir
dtype: string
- name: markdown
dtype: string
- name: inference_info
dtype: string
splits:
- name: train
num_bytes: 5806763.0
num_examples: 10
download_size: 5809851
dataset_size: 5806763.0
---
# Document OCR using DeepSeek-OCR
This dataset contains markdown-formatted OCR results from images in [NealCaren/InkBench](https://huggingface.co/datasets/NealCaren/InkBench) using DeepSeek-OCR.
## Processing Details
- **Source Dataset**: [NealCaren/InkBench](https://huggingface.co/datasets/NealCaren/InkBench)
- **Model**: [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR)
- **Number of Samples**: 10
- **Processing Time**: 1.6 min
- **Processing Date**: 2026-03-05 20:59 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 \\
NealCaren/InkBench \\
<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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