Image Text Detection

Image Text Detection

A Streamlit app that reads handwritten text from a picture with the microsoft TrOCR handwriting models. It suits people who want to try handwriting recognition in a browser.

Made by ajitrajasekharan 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: Image Text Detection is a Streamlit app on Hugging Face Spaces that reads text from pictures. It names two models, microsoft/trocr-base-handwritten and microsoft/trocr-large-handwritten, which are trained for handwriting. The code is app.py, and requirements.txt lists the packages.

Who it suits: It suits people who need to turn handwritten text in an image into typed text, and learners who want to see how a text recognition model sits behind a Streamlit page.

Industry | Who uses it | How they use it | Example
Administration | Office staff | Try reading a handwritten note from a picture | Open the Space and give it a photo of a note
Education | Students | Compare the base and large handwriting models | Run the same image through the demo
Software | Python developers | Read app.py to see how the models are called | Download the Space and open app.py

Anyone who needs batch processing or a stated accuracy can skip it, since the source gives neither.

Where to use it:

• A web browser, straight from the Space page on Hugging Face
• Windows, macOS or Linux with Python, as a local copy

Use case | Who | What they get back
Read handwriting from a picture | Anyone with a handwritten note | The recognised text shown on the page
Study a text recognition demo | Learners | An app file that calls two named models

How to set it up: Using it online needs nothing. A local copy needs these first.

• Python and pip
• The huggingface_hub package, to fetch the files
• Streamlit, and the packages in requirements.txt

1. Install the Hugging Face Hub package.
2. Download the Space into a folder called Image-Text-Detection.
3. Move into that folder.
4. Install Streamlit, then the packages from requirements.txt.
5. Start the app with Streamlit.

pip install -U huggingface_hub
hf download ajitrajasekharan/Image-Text-Detection --repo-type space --local-dir Image-Text-Detection
cd Image-Text-Detection
pip install streamlit
pip install -r requirements.txt
streamlit run app.py

How to use it: Open the Space page on Hugging Face and give it a picture that holds handwriting. For a local copy, run streamlit run app.py in the folder and open the page it starts.

Example: You give the app a photo of a handwritten line. It runs the TrOCR handwriting model on the picture and shows the text it reads.

For newcomers and experts: If you are new, take a clear photo of one short handwritten line and try it first. Optical character recognition means a program reads the letters in a picture and gives them back as text.

If you know the field, the two TrOCR checkpoints are a base and a large size trained for handwriting. The larger one trades speed and memory for a bigger model. The source does not say how the app chooses between them.

You give the app a picture with handwriting
app.py runs a TrOCR handwriting model
The recognised text shows on the page

Good to know:

• The README gives no description, so how the app picks a model is clear only when you try it
• It needs Streamlit and the packages in requirements.txt
• License: mit
• Cost to run: none named
• Data it reaches: the pictures you give it and the two TrOCR models it names

On Hugging Face, 13 November 2026

• Likes: 6
• Built with: streamlit
• License: mit

Links

• Space: https://huggingface.co/spaces/ajitrajasekharan/Image-Text-Detection
• Files: https://huggingface.co/spaces/ajitrajasekharan/Image-Text-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.