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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: 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: 4866518
num_examples: 50
download_size: 4853285
dataset_size: 4866518
---
# Document OCR using GLM-OCR
This dataset contains OCR results from images in [biglam/bpl-card-catalog](https://huggingface.co/datasets/biglam/bpl-card-catalog) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
## Processing Details
- **Source Dataset**: [biglam/bpl-card-catalog](https://huggingface.co/datasets/biglam/bpl-card-catalog)
- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
- **Task**: text recognition
- **Number of Samples**: 50
- **Processing Time**: 21.2 min
- **Processing Date**: 2026-02-22 15:39 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 \
biglam/bpl-card-catalog \
<output-dataset> \
--image-column image \
--batch-size 16 \
--task ocr
```
Generated with [UV Scripts](https://huggingface.co/uv-scripts)
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