Home Projects SimpleHTR
SimpleHTR
Python

SimpleHTR

Handwritten Text Recognition (HTR) system implemented with TensorFlow.

by githubharald · GitHub
Stars
Forks
License
Created
Last commit
Language
deep-learninghandwritten-text-recognitionmachine-learningMITPython
View on GitHub
In plain words

Recognize handwritten text from images and convert it into digital text using a trained model.

You maintain this project?

Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.

Claim this page →
SimpleHTR — GitHub preview card
📈 Star history
2.18k2.17k
2026-07-072026-08-31
📈 Track SimpleHTR

Get an email alert on its next release or when it starts trending — never miss the moment.

Free · no card · unsubscribe anytime
Get email alerts →
📄 About

Handwritten Text Recognition (HTR) system implemented with TensorFlow.

SimpleHTR has 2.2k stars on GitHub. It has been forked 913 times. SimpleHTR is written mainly in Python. It has been in active development since 2018. SimpleHTR is available under the MIT license. Its main topics are deep-learning, handwritten-text-recognition, machine-learning, ocr.

Frequently asked questions

What is SimpleHTR?

Handwritten Text Recognition (HTR) system implemented with TensorFlow.

Is SimpleHTR open source?

SimpleHTR is an open-source project. It is released under the MIT license.

Is SimpleHTR free?

Yes. SimpleHTR is free and open source — you can use, modify and self-host it.

What license does SimpleHTR use?

SimpleHTR is available under the MIT license.

What language is SimpleHTR written in?

SimpleHTR is written mainly in Python.

🏅 Maintainer of this project?
olud.ai badge — SimpleHTR

Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.

[![olud.ai](https://olud.ai/badge.php?tool=githubharald-simplehtr)](https://olud.ai/project/githubharald-simplehtr.html)
More badge options →
🧬 Shares DNA with🧬 View the DNA map →

Measured from GitHub topics shared by both projects, weighted by how rare each topic is.