The prospect of accurately transcribing mathematical expression into a markup representation is enticing because it opens the doors for bringing new life to old mathematical texts or those for which the source code is unavailable.

We make that dream a reality with this project, where we build on Sumeet Singh's work porting the model to Python3 and bringing our own dataset of 170,000 examples.

Dynamic Content

Sumeet S. Singh's Project Site

Static Content

Complete Dataset

Research Paper

Team

Directory Structure

TEXNET
│   
│      
│
└───data -> has to be made by the user (extract data folder in there)
│   │   
│   │   
│   │
│   └───formula_images
│       │   *.png    
│   
└───src -> main directory of where model will be ran
│   │   
│   │   
│   │
│   └───tools
│   │   │   prepareData.py -> run first when images are placed in inference folder
│   │   │   predict.py -> run this script after running the model based on the script outputted from prepareData.py
│   │   │   predictions.csv -> outputted csv with filenames and LaTeX predictions
│   │   
│   │   
│   │
│   └───tb_metrics -> main logging directory where logs from model training will be stored
│   │
│   │
│   │  run.py -> main file that runs the model, has settable hyperparameters and variables that need to be specified based on the training machine
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└───predictions
│   │   
│   │   
│   │
│   └───formula_images -> paste images to perform inference on here before running inference scripts