Date of Award
Santa Clara : Santa Clara University
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 depending 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 involves a C++ library that combines image pre-processing, optical character recognition, 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.
Matsunaga, Nate and Sullivan, Rick, "Image processing for the extraction of nutritional information from food labels" (2015). Computer Science and Engineering Senior Theses. 42.