This is a TensorFlow port of harvardnlp's im2markup project. The model accepts images of mathematical equations typeset in Latex, and outputs the markup used to generate those images. It contains the im2markup project as a git submodule to make use of its preprocessing scripts.
Briefly, the model uses a convolutional network followed by a Bi-RNN row encoder as an encoder, and an LSTM with an attention mechanism as a decoder.
To run, clone this repository, run the prep.sh (will install nodeJS if not already installed) script in the data directory, and then run python im2latex.py.
Unfortunately, as TensorFlow requires more memory than Torch, this project was not able to use the complete dataset used by the im2markup project. Assuming a batch size of 20, about one fourth of the dataset consisted of images that were too large to fit on a Titan X GPU. Still, over a period of 100 epochs, each taking approximately 100 minutes, the model seemed to saturate at 64.0% validation accuracy at around epoch 60. Test accuracy was only measured after 100 epochs, and was 64.9%. The im2markup project made use of beam search to measure test accuracy, which has not been implemented in this project yet. This, and the inability to use images from the full dataset, may help to explain the difference in performance relative to the im2markup project, which achieved 75% test accuracy on the full dataset.
I hope to look into implementing beam search, and running this model on AWS, where I can distribute the model across multiple GPUs and hopefully run it on the full dataset.