Texify converts equations and surrounding text into markdown and LaTeX that can be rendered by MathJax ($$ and $ are delimiters). It will work with images or pdfs, and can run on CPU, GPU, or MPS.

The closest open source comparisons to texify are pix2tex and nougat, although they're designed for different purposes:

• Pix2tex is designed for block LaTeX equations, and hallucinates more on text. Texify can work with inline equations and text.
• Nougat is designed to OCR entire pages, and hallucinates more on small images. Texify is optimized for equations and small page regions.

Pix2tex is trained on im2latex, and nougat is trained on arxiv. Texify is trained on a broader set of web data, and works on a range of images.

See more details in the benchmarks section.

Discord is where we discuss future development.

Note I added spaces after _ symbols because Github math formatting is broken.

Detected Text The potential $V_{i}$ of cell $\mathcal{C}_ {j}$ centred at position $\mathbf{r}_ {i}$ is related to the surface charge densities $\sigma_ {j}$ of cells $\mathcal{E}_ {j}$ $j\in[1,N]$ through the superposition principle as:

$$V_ {i},=,\sum_ {j=0}^{N},\frac{\sigma_ {j}}{4\pi\varepsilon_ {0}},\int_{\mathcal{E}_ {j}}\frac{1}{\left|\mathbf{r}_ {i}-\mathbf{r}^{\prime}\right|},\mathrm{d}^{2}\mathbf{r}^{\prime},=,\sum_{j=0}^{N},Q_ {ij},\sigma_{j},$$

where the integral over the surface of cell $\mathcal{C}_ {j}$ only depends on $\mathcal{C}{j}$ shape and on the relative position of the target point $\mathbf{r}_ {i}$ with respect to $\mathcal{C}_ {j}$ location, as $\sigma_ {j}$ is assumed constant over the whole surface of cell $\mathcal{C}_ {j}$.

This has been tested on Mac and Linux (Ubuntu and Debian). You'll need python 3.10+ and PyTorch. You may need to install the CPU version of torch first if you're not using a Mac or a GPU machine. See here for more details.

pip install texify

Model weights will automatically download the first time you run it.

• Inspect the settings in texify/settings.py. You can override any settings with environment variables.
• Your torch device will be automatically detected, but you can override this. For example, TORCH_DEVICE=cuda or TORCH_DEVICE=mps.

I've included a streamlit app that lets you interactively select and convert equations from images or PDF files. Run it with:

texify_gui

The app will allow you to select the specific equations you want to convert on each page, then render the results with KaTeX and enable easy copying.

You can OCR a single image or a folder of images with:

texify /path/to/folder_or_file --max 8 --json_path results.json

• --max is how many images in the folder to convert at most. Omit this to convert all images in the folder.
• --json_path is an optional path to a json file where the results will be saved. If you omit this, the results will be saved to data/results.json.

You can import texify and run it in python code:

from texify.inference import batch_inference
from texify.model.model import load_model
from texify.model.processor import load_processor
from PIL import Image

model = load_model()
processor = load_processor()
img = Image.open("test.png") # Your image name here
results = batch_inference([img], model, processor)

If you want to develop texify, you can install it manually:

• git clone https://github.com/VikParuchuri/texify.git
• cd texify
• poetry install # Installs main and dev dependencies

OCR is complicated, and texify is not perfect. Here are some known limitations:

• Texify will OCR equations and surrounding text, but is not good for general purpose OCR. Think sections of a page instead of a whole page.
• Texify was mostly trained with 96 DPI images, and only at a max 420x420 resolution. Very wide or very tall images may not work well.
• It works best with English, although it should support other languages with similar character sets.
• The output format will be markdown with embedded LaTeX for equations (close to Github flavored markdown). It will not be pure LaTeX.

Benchmarking OCR quality is hard - you ideally need a parallel corpus that models haven't been trained on. I sampled from arxiv and im2latex to create the benchmark set.

Each model is trained on one of the benchmark tasks:

• Nougat was trained on arxiv, possibly the images in the benchmark.
• Pix2tex was trained on im2latex.
• Texify was trained on im2latex. It was trained on arxiv, but not the images in the benchmark.

Although this makes the benchmark results biased, it does seem like a good compromise, since nougat and pix2tex don't work as well out of domain. Note that neither pix2tex or nougat is really designed for this task (OCR inline equations and text), so this is not a perfect comparison.

You can benchmark the performance of texify on your machine.

• Follow the manual install instructions above.
• If you want to use pix2tex, run pip install pix2tex
• If you want to use nougat, run pip install nougat-ocr
• Download the benchmark data here and put it in the data folder.
• Run benchmark.py like this:

python benchmark.py --max 100 --pix2tex --nougat --data_path data/bench_data.json --result_path data/bench_results.json

This will benchmark marker against pix2tex and nougat. It will do batch inference with texify and nougat, but not with pix2tex, since I couldn't find an option for batching.

• --max is how many benchmark images to convert at most.
• --data_path is the path to the benchmark data. If you omit this, it will use the default path.
• --result_path is the path to the benchmark results. If you omit this, it will use the default path.
• --pix2tex specifies whether to run pix2tex (Latex-OCR) or not.
• --nougat specifies whether to run nougat or not.

Texify was trained on latex images and paired equations from across the web. It includes the im2latex dataset. Training happened on 4x A6000s for 2 days (~6 epochs).

This model is trained on top of the openly licensed Donut model, and thus can be used for commercial purposes. Model weights are licensed under the CC BY-SA 4.0 license.

This work would not have been possible without lots of amazing open source work. I particularly want to acknowledge Lukas Blecher, whose work on Nougat and pix2tex was key for this project. I learned a lot from his code, and used parts of it for texify.

• im2latex - one of the datasets used for training
• Donut from Naver, the base model for texify
• Nougat - I used the tokenizer from Nougat
• Latex-OCR - The original open source Latex OCR project