rapid_latex_ocr is a tool to convert formula images to latex format.

The reasoning code in the repo is modified from LaTeX-OCR, the model has all been converted to ONNX format, and the reasoning code has been simplified, Inference is faster and easier to deploy.

The repo only has codes based on ONNXRuntime or OpenVINO inference in onnx format, and does not contain training model codes. If you want to train your own model, please move to LaTeX-OCR.

If it helps you, please give a little star ⭐ or sponsor a cup of coffee (click the link in Sponsor at the top of the page)

🔥 Model Conversion Notes 👉 ConvertLaTeXOCRToONNX

flowchart LR

A(Preprocess Formula\n ProcessLaTeXFormulaTools) --> B(Train\n LaTeX-OCR) --> C(Convert \n ConvertLaTeXOCRToONNX) --> D(Deploy\n RapidLaTeXOCR)

click A "https://github.com/SWHL/ProcessLaTeXFormulaTools" _blank
click B "https://github.com/lukas-blecher/LaTeX-OCR" _blank
click C "https://github.com/SWHL/ConvertLaTeXOCRToONNX" _blank
click D "https://github.com/RapidAI/RapidLaTeXOCR" _blank

pip install rapid_latex_ocr

from rapid_latex_ocr import LatexOCR

model = LatexOCR()

img_path = "tests/test_files/6.png"
with open(img_path, "rb") as f:
    data = f.read()

res, elapse = model(data)

print(res)
print(elapse)

$ rapid_latex_ocr tests/test_files/6.png

# {\\frac{x^{2}}{a^{2}}}-{\\frac{y^{2}}{b^{2}}}=1
# 0.47902780000000034

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

If you want to sponsor the project, you can directly click the Buy me a coffee image, please write a note (e.g. your github account name) to facilitate adding to the sponsorship list below.

This project is released under the MIT license.