--- title: "Image Text Detection" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/6504a0d2-38d1-463d-afff-703db411ba4b" updated: "2026-11-12" --- # Image Text Detection > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. Image Text Detection is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by ajitrajasekharan ## 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. ```sh 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```flow 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. ```` ## What is in it 1 files, 4 KB, downloaded as a zip. ```text README.md 4 KB ``` ## Source [ajitrajasekharan/Image-Text-Detection on Hugging Face](https://huggingface.co/spaces/ajitrajasekharan/Image-Text-Detection) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | Image Text Detection (this page) | 6 | streamlit | mit | 2022-02-24 | | [TorchCAM](https://mindket.com/p/b3cf467d-789d-44bf-8e51-33b548437856) | 16 | docker | apache-2.0 | 2026-11-10 | | [Anime Recommendation System](https://mindket.com/p/5f7580f9-e9b9-47f7-8395-f8495f13c8ed) | 9 | streamlit | apache-2.0 | 2025-02-10 | | [Spacy Ner](https://mindket.com/p/24e15a50-b5da-42a7-8c3f-6ce2b280891c) | 2 | streamlit | cc-by-nc-sa-4.0 | 2022-02-15 | | [NER Biomedical PHI Ensemble](https://mindket.com/p/a616a692-3657-41b6-8e6f-61db2b7d6320) | 2 | streamlit | mit | 2022-06-09 | | [Harry Potter Q A](https://mindket.com/p/7a0ca5b6-7021-484b-8edd-63f52c5c3343) | 1 | streamlit | afl-3.0 | 2022-03-11 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2022-02-24 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ajitrajasekharan/Image-Text-Detection on Hugging Face](https://huggingface.co/spaces/ajitrajasekharan/Image-Text-Detection) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/6504a0d2-38d1-463d-afff-703db411ba4b) - [Its page on Mindket](https://mindket.com/p/6504a0d2-38d1-463d-afff-703db411ba4b)