The initial inspiration stemmed from having to painstakingly enter mathematical notations using an online keyboard for quantitative research papers. Quantitative research papers require a huge amount of math symbols & notations but it oftentimes takes a lot of time to type out, especially for students who are using free tools. Handwriting math is more intuitive and flexible but the written results are not optimal for publishability. Furthermore, digital notes have become more prevalent but typing out math notes is challenging as there is no simple way to type notations and special symbols in commonly used text editor applications such as Word, Pages or Google Docs. As such, our handwritten math translator aims to serve the needs of college and high school students who study STEM.
What it does
M.TeX digitizes handwritten math to mathematical notation and LaTeX form. You upload an image of your handwritten math and get to copy-paste the text-editable version to anywhere you'd like!
How we built it
We designed the wireframes using Figma and used HTML and CSS to build the outline of the page and bootstrap for the layout. Then, we built the backend Python class object and called it from a flask server.
Challenges we ran into
- Trying to connect the frontend and backend development
- Processing an input image from the end-user without any server knowledge
- Developing a visual learning algorithm with no background in visual learning
Accomplishments that we're proud of
Creating something that actually kinda works without having relevant knowledge prior to the weekend!
What we learned
- Connecting various pieces of a project together
- Gaining a deeper knowledge of Python libraries and understanding of new packages and modules
- Working and programming with other people
What's next for M.Tex
- Improving the prediction accuracy of our image recognition model
- Including outputting LaTeX into our functionality