Signature Detection

Signature Detection

Finds handwritten signatures in images with a small YOLOv8 detector and runs on a CPU. It suits document teams and developers who need to check whether a page carries a signature.

Made by tech4humans 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: Signature Detection is a demo that finds handwritten signatures. It is built around a small YOLOv8 model trained for this one job, published as tech4humans/yolov8s-signature-detector. The Space runs on a CPU, so no GPU is needed.

Who it suits: Teams that handle many scanned documents and need to know whether a signature is present. It also suits developers building a document pipeline who want to try a ready signature detector first.

Title: Who checks documents for signatures
Industry | Who uses it | How they use it | Example
Back office | Document processing teams | Check scanned pages for handwritten signatures | Sort signed pages from unsigned ones
Legal | Contract reviewers | Locate the signature block on a page | Confirm a scanned contract carries a signature
Finance | Operations staff | Screen forms before filing | Flag forms that arrive without a signature
Software | Developers | Try a signature detector before adding one to a pipeline | Test the model on sample scans

If you need to verify whose signature it is, you can skip it, since the source describes detection only.

Where to use it:

• A web browser: the Space runs on Hugging Face with nothing to install
• Windows, macOS or Linux with Python, from a local copy

Use case | Who | What they get back
Find a signature on a page | Reviewers | The place on the image where a handwritten signature appears
Try the model quickly | Developers | A working detector in a browser tab
Build a signed-or-not check | Operations teams | A way to see which pages carry a signature

How to set it up:

• Python with pip
• The Python packages in requirements.txt

1. Install the Hugging Face hub package and download the Space into a folder.
2. Move into that folder.
3. Install Gradio 5.12.0, then the packages from requirements.txt.
4. Start the app with Python.

pip install -U huggingface_hub
hf download tech4humans/signature-detection --repo-type space --local-dir signature-detection
cd signature-detection
pip install gradio==5.12.0
pip install -r requirements.txt
python app.py

How to use it: Open the Space in your browser, or start the local copy with the command below and open the page Gradio serves. Give it an image of a document and look at what it finds.

python app.py

Example: You give it a scanned page that has a handwritten signature near the bottom. The detector looks for signatures on the image and shows you where it found one.

For newcomers and experts: If you are new to this, open the Space and try one scanned page that you know is signed. Then try a blank page and compare.

If you know the field, this is object detection with a YOLOv8s model, with the weights in tech4humans/yolov8s-signature-detector and a matching dataset, tech4humans/signature-detection. The repository holds a detector module, a constants file and a metrics storage module next to the Gradio app. A small model keeps it light on CPU.

You give it an image of a document
The YOLOv8s model scans the image
Handwritten signatures are located
The result is shown in the page

Good to know:

• Hardware: the Space runs on cpu-basic at Hugging Face
• Model and data: the Space names one model and one dataset, both from tech4humans
• License: agpl-3
• Cost to run: none named

On Hugging Face, 13 November 2026

• Likes: 12
• Built with: gradio
• License: agpl-3.0

Links

• Space: https://huggingface.co/spaces/tech4humans/signature-detection
• Files: https://huggingface.co/spaces/tech4humans/signature-detection/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.