Getting Started

Project Abstract:

While handwriting provides an efficient means to write mathematical symbols quickly, it is a poor medium for rapid exchange and editing of documents. Meanwhile, advanced typesetting systems like LaTeX and MathML have provided an environment where mathematical symbols can be typeset with precision, but at the cost of typing time and a steep learning curve. In order to facilitate the exchange, preservation and ease of editing of mathematical documents, we propose a method of offline handwritten equational recognition. Our system takes a handwritten document, for example a students calculus homework, then partitions, classifies and parses the document into LaTeX.

Current Progress:

We currently have a small toy dataset, and are attempting to get classifier code we have now to work with it. We are using this time to figure out what we think is a reasonable scope of mathematical symbols before we create a larger dataset.

Soon, we should have a good idea what qualities we would like in our dataset. At that point we will use Mechanical Turk.

In addition, we are researching localization / bounding-box techniques. Once we have a dataset, the next step will be to focus on the partitioning of data.