Package: texify
TexifyTexify is an OCR model that converts images or pdfs containing math into markdown and LaTeX that can be rendered by MathJax ($$ and $ are delimiters).  It can run on CPU, GPU, or MPS.https://github.com/VikParuchuri/texify/assets/913340/882022a6-020d-4796-af02-67cb77bc084cTexify can work with block equations, or equations mixed with text (inline).  It will convert both the equations and the text.The closest open source comparisons to texify arepix2texandnougat, although they're designed for different purposes:Pix2tex is designed only for block LaTeX equations, and hallucinates more on text.Nougat is designed to OCR entire pages, and hallucinates more on small images only containing math.Pix2tex is trained on im2latex, and nougat is trained on arxiv.  Texify is trained on a more diverse set of web data, and works on a range of images.See more details in thebenchmarkssection.CommunityDiscordis where we discuss future development.ExamplesNoteI added spaces after _ symbols and removed , becauseGithub math formatting is broken.Detected TextThe potential $V_ i$ of cell $\mathcal{C}_ i$ centred at position $\mathbf{r}_ i$ is related to the surface charge densities $\sigma_ j$ of cells $\mathcal{C}_ j$ $j\in[1,N]$ through the superposition principle as: $$V_ i = \sum_ {j=0}^{N} \frac{\sigma_ j}{4\pi\varepsilon_ 0} \int_ {\mathcal{C}_ j} \frac{1}{|\mathbf{r}_ i-\mathbf{r}'|} \mathrm{d}^2\mathbf{r}' = \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 $\mathcal{C}_ j$.ImageOCR Markdown112233InstallationYou'll need python 3.9+ and PyTorch. You may need to install the CPU version of torch first if you're not using a Mac or a GPU machine.  Seeherefor more details.Install with:`pip install texify`Model weights will automatically download the first time you run it.UsageInspect the settings intexify/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=cudaorTORCH_DEVICE=mps.Usage tipsDon't make your boxes too small or too large.  See the examples and the video above for good crops.Texify is sensitive to how you draw the box around the text you want to OCR. If you get bad results, try selecting a slightly different box, or splitting the box into 2+.  You can also try changing theTEMPERATUREsetting.Sometimes, KaTeX won't be able to render an equation (red error), but it will still be valid LaTeX.  You can copy the LaTeX and render it elsewhere.App for interactive conversionI've included a streamlit app that lets you interactively select and convert equations from images or PDF files.  Run it with:texify_guiThe 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.Convert imagesYou can OCR a single image or a folder of images with:texify /path/to/folder_or_file --max 8 --json_path results.json--maxis how many images in the folder to convert at most.  Omit this to convert all images in the folder.--json_pathis an optional path to a json file where the results will be saved.  If you omit this, the results will be saved todata/results.json.--katex_compatiblewill make the output more compatible with KaTeX.Import and runYou 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)Seetexify/output.py:replace_katex_invalidif you want to make the output more compatible with KaTeX.Manual installIf you want to develop texify, you can install it manually:git clone https://github.com/VikParuchuri/texify.gitcd texifypoetry install# Installs main and dev dependenciesLimitationsOCR is complicated, and texify is not perfect.  Here are some known limitations:The OCR is dependent on how you crop the image.  If you get bad results, try a different selection/crop.  Or try changing theTEMPERATUREsetting.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.BenchmarksBenchmarking 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.ModelBLEU ⬆METEOR ⬆Edit Distance ⬇pix2tex0.3826590.5433630.352533nougat0.6976670.6683310.288159texify0.8423490.8857310.0651534Running your own benchmarksYou can benchmark the performance of texify on your machine.Follow the manual install instructions above.If you want to use pix2tex, runpip install pix2texIf you want to use nougat, runpip install nougat-ocrDownload the benchmark datahereand put it in thedatafolder.Runbenchmark.pylike this:python benchmark.py --max 100 --pix2tex --nougat --data_path data/bench_data.json --result_path data/bench_results.jsonThis 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.--maxis how many benchmark images to convert at most.--data_pathis the path to the benchmark data.  If you omit this, it will use the default path.--result_pathis the path to the benchmark results.  If you omit this, it will use the default path.--pix2texspecifies whether to run pix2tex (Latex-OCR) or not.--nougatspecifies whether to run nougat or not.TrainingTexify was trained on latex images and paired equations from across the web.  It includes theim2latexdataset.  Training happened on 4x A6000s for 2 days (~6 epochs).Commercial usageThis model is trained on top of the openly licensedDonutmodel, and thus can be used for commercial purposes.  Model weights are licensed under theCC BY-SA 4.0license.ThanksThis work would not have been possible without lots of amazing open source work.  I particularly want to acknowledgeLukas 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 trainingDonutfrom Naver, the base model for texifyNougat- I used the tokenizer from NougatLatex-OCR- The original open source Latex OCR project