File size: 2,193 Bytes
3a5a459 dc4b7fc 3a5a459 f44096a 3a5a459 dc4b7fc 3a5a459 dc4b7fc 3a5a459 dc4b7fc 3a5a459 dc4b7fc 3a5a459 dc4b7fc 3a5a459 6deff08 dc4b7fc 3a5a459 dc4b7fc 3a5a459 dc4b7fc 3a5a459 dc4b7fc 3a5a459 6deff08 | 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 88 89 90 91 92 | ---
tags:
- ocr
- document-processing
- glm-ocr
- markdown
- uv-script
- generated
configs:
- config_name: dots-ocr
data_files:
- split: train
path: dots-ocr/train-*
dataset_info:
config_name: dots-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: 5797728
num_examples: 10
download_size: 5802612
dataset_size: 5797728
---
# Document OCR using GLM-OCR
This dataset contains OCR results from images in [NealCaren/InkBench](https://huggingface.co/datasets/NealCaren/InkBench) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
## Processing Details
- **Source Dataset**: [NealCaren/InkBench](https://huggingface.co/datasets/NealCaren/InkBench)
- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
- **Task**: text recognition
- **Number of Samples**: 10
- **Processing Time**: 2.1 min
- **Processing Date**: 2026-03-05 21:00 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
```bash
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
NealCaren/InkBench \
<output-dataset> \
--image-column image \
--batch-size 16 \
--task ocr
```
Generated with [UV Scripts](https://huggingface.co/uv-scripts)
|