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Image to LaTeX (Seq2seq + Attention with Beam Search) - Tensorflow
| Date | Stars |
|---|---|
| 2026-07-24 | 462 |
| 2026-07-25 | 462 |
| 2026-07-28 | 462 |
| 2026-07-30 | 462 |
| 2026-08-06 | 462 |
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# Im2Latex Seq2Seq model with Attention + Beam Search for Image to LaTeX, similar to [Show, Attend and Tell](https://arxiv.org/abs/1502.03044) and [Harvard's paper and dataset](http://lstm.seas.harvard.edu/latex/). Check the [blog post](https://guillaumegenthial.github.io/image-to-latex.html). ## Install Install pdflatex (latex to pdf) and ghostsript + [magick](https://www.imagemagick.org/script/install-source.php ) (pdf to png) on Linux ``` make install-linux ``` (takes a while ~ 10 min, installs from source) On Mac, assuming you already have a LaTeX distribution installed, you should have pdflatex and ghostscript installed, so you just need to install magick. You can try ``` make install-mac ``` ## Getting Started We provide a small dataset just to check the pipeline. To build the images, train the model and evaluate ``` make small ``` You should observe that the model starts to produce reasonable patterns of LaTeX after a few minutes. ## Data We provide the pre-processed formulas from [Harvard](https://zenodo.org/record/56198#.V2p0KTXT6eA) but you'll need to produce the images from those formulas (a few hours on a laptop). ``` make build ``` Alternatively, you can download the [prebuilt dataset from Harvard](https://zenodo.org/record/56198#.V2p0KTXT6eA) and use their preprocessing scripts found [here](https://github.com/harvardnlp/im2markup) ## Training on the full dataset If you already did `make build` you can just train and evaluate the model with the following commands ``` make train make eval ``` Or, to build the images from the formulas, train the model and evaluate, run ``` make full ``` ## Details 1. Build the images from the formulas, write the matching file and extract the vocabulary. __Run only once__ for a dataset ``` python build.py --data=configs/data.json --vocab=configs/vocab.json ``` 2. Train ``` python train.py --data=configs/data.json --vocab=configs/vocab.json --training=configs/training.json --model=configs/model.json --output=results/full/ ``` 3. Evaluate the text metrics ``` python evaluate_txt.py --results=results/full/ ``` 4. Evaluate the image metrics ``` python evaluate_img.py --results=results/full/ ``` (To get more information on the arguments, run) ``` python file.py --help ```
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
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