This repository contains code and documentation for the senior thesis by Rick Sullivan and Nate Matsunaga. This project won best in session for Computer Science & Engineering in June 2015 as determined by industry judges. The thesis can be found here.
Current techniques for tracking nutritional data require undesirable amounts of either time or man- power. People must choose between tediously recording and updating dietary information or de- pending on unreliable crowd-sourced or costly maintained databases. Our project looks to overcome these pitfalls by providing a programming interface for image analysis that will read and report the information present on a nutrition label directly. Our solution provides a library that combines image pre-processing, optical character recog- nition, and post-processing techniques to pull the relevant information from an image of a nutrition label. We apply an understanding of a nutrition label’s content and data organization to approach the accuracy of traditional data-entry methods. Our system currently provides around 80% accuracy for most label images, and we will continue to work to improve our accuracy.