Advisor: Dr. Chris Pollett

Description:

Many research papers in mathematics, computer science, and physics are written in LaTeX format. While writing technical papers or articles, there are some scenarios where the text to be written is a mathematical equation. Writing a mathematical equation in LaTeX format takes a lot more time compared to writing the same equation on a paper. The time-consuming approach of converting the equation written on paper to LaTeX format can be automated and optimized.

This project will develop a tool which will take an image of a mathematical equation as an input and will attempt to output the corresponding mathematical equation in the LaTeX form.

Schedule:

Deliverables:

The full project will be done when CS298 is completed. The following will be done by the end of CS297:

Deliverable #1: Famous Mathematical Equations to Corresponding LaTeX

Deliverable #2: Install TensorFlow and go through MNIST demo

Deliverable #3: Explore data generation approaches

Deliverable #4: Decide the Architectural Diagram

Deliverable #5: Complete the CS297 Final Report.

References:

[AuthorYear] Reference_work. Author_Names. Publisher. Year.

Yuntian Deng, Anssi Kanervisto, Jeffrey Ling, Alexander M. Rush; "Image-to-Markup Generation with Coarse-to-Fine Attention"; Proceedings of the 34th International Conference on Machine Learning, 2017

Oriol Vinyals, Alexander Toshev, Samy Bengio, Dumitru Erhan; "Show and Tell: A Neural Image Caption Generator"; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015