GLM OCR Demo

GLM OCR Demo

Reads text and structure from document images with the GLM-OCR model, in the browser. For anyone who wants to try OCR on complex documents without setting anything up.

Made by prithivMLmods on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face.

About

What it is: GLM OCR Demo lets you try the GLM-OCR model in your browser. It is a multimodal OCR model aimed at complex document understanding, so you give it a document image and it reads the content for you. The demo is built with Gradio.

Who it suits: It suits people who want to see how GLM-OCR handles real documents before they build anything around it.

Industry | Who uses it | How they use it | Example
Administration | Office staff | Pull text out of scanned pages | Try a scanned form to see what is read
Research | Researchers | Test OCR on complex documents | Compare how the model reads a dense page
Development | Developers | Try the model before wiring it into an app | Check output on a sample document
Education | Students | Convert pictured notes to text | Try a photo of a printed handout

You can skip it if you need a hosted OCR service with guarantees, since this is a demo.

Where to use it:

• A web browser, on the Space's page at Hugging Face
• Your own computer, on Windows, macOS or Linux with Python
• Any MCP client, since the Space is tagged as MCP

Use case | Who | What they get back
Read a scanned page | Office staff | The text the model finds in the image
Test complex layouts | Researchers | A view of how the model handles them
Try the examples folder | Newcomers | A quick look at what the model can do

How to set it up:

• Nothing to install if you use the Space online
• Python, if you run a copy on your own computer
• The huggingface_hub package, installed with pip
• The Python packages in requirements.txt

1. Open the Space's page on Hugging Face to use it online.
2. To run a copy, install huggingface_hub and download the Space into a folder.
3. Move into that folder and install Gradio at the pinned version, then the listed packages.
4. Start the app with Python.

pip install -U huggingface_hub
hf download prithivMLmods/GLM-OCR-Demo --repo-type space --local-dir GLM-OCR-Demo
cd GLM-OCR-Demo
pip install gradio==6.30.0
pip install -r requirements.txt
python app.py

How to use it: Open the page, add a document image, and run it. The repository includes an examples folder you can try first. On your own copy, start the app with this command and open the local page Gradio prints.

python app.py

Example: Upload a photo of a printed page and run the demo. You get back the text the GLM-OCR model reads from the page.

For newcomers and experts: If you are new, open the Space online and try one of the sample documents first. That shows what comes back before you use your own files.

If you know the field, the demo wraps the zai-org GLM-OCR model in a Gradio app. It runs on GPU hardware at Hugging Face and carries an MCP tag. It has a separate pre-requirements file next to requirements.txt, so install order may matter on your own machine.

You add a document image
Gradio passes it to the GLM-OCR model
The model reads the content
The result shows on the page

Good to know:

• License: apache-2
• Cost to run: none named. The online Space runs on Hugging Face hardware.
• Data it reaches: the images you give it and the zai-org GLM-OCR model it names.
• Not for: production OCR with uptime or accuracy guarantees. It is a demo, and the source gives no accuracy numbers.

On Hugging Face, 13 November 2026

• Likes: 128
• Built with: gradio
• License: apache-2.0

Links

• Space: https://huggingface.co/spaces/prithivMLmods/GLM-OCR-Demo
• Files: https://huggingface.co/spaces/prithivMLmods/GLM-OCR-Demo/tree/main

Try it

Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself.