Document OCR using GLM-OCR

This dataset contains OCR results from images in biglam/rubenstein-manuscript-catalog using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.

Processing Details

• Source Dataset: biglam/rubenstein-manuscript-catalog
• Model: zai-org/GLM-OCR
• Task: text recognition
• Number of Samples: 50
• Processing Time: 5.7 min
• Processing Date: 2026-02-15 00:40 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

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
    biglam/rubenstein-manuscript-catalog \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --task ocr

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