---
tags:
- ocr
- document-processing
- glm-ocr
- markdown
- uv-script
- generated
- hf-jobs
---

# Document OCR using GLM-OCR

This dataset contains OCR results from images in [divinity-library/thg-test-01](https://huggingface.co/datasets/divinity-library/thg-test-01) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.

## Processing Details

- **Source Dataset**: [divinity-library/thg-test-01](https://huggingface.co/datasets/divinity-library/thg-test-01)
- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
- **Task**: text recognition
- **Number of Samples**: 6
- **Processing Time**: 1.9 min
- **Processing Date**: 2026-10-28 02:37 UTC

### Configuration

- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 16
- **Max Model Length**: 8,192 tokens
- **Max Output Tokens**: 8,192
- **Temperature**: 0.01
- **Top P**: 1e-05
- **GPU Memory Utilization**: 80.0%

## Model Information

GLM-OCR is a compact, high-performance OCR model:
- 0.9B parameters
- 94.62% on OmniDocBench V1.5
- CogViT visual encoder + GLM-0.5B language decoder
- Multi-Token Prediction (MTP) loss for efficiency
- Multilingual: zh, en, fr, es, ru, de, ja, ko
- MIT licensed

## 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

## Reproduction

Produced on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs) (`a10g-small`) with the [`glm-ocr.py`](https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py) recipe from [uv-scripts](https://huggingface.co/uv-scripts). Run it yourself:

```bash
hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
    divinity-library/thg-test-01 \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --task ocr
```