Detecting and comparing Kanban boards using Computer Vision

Sammanfattning: This thesis investigates the problem of detecting and tracking sticky notes on Kanban boards using classical computer vision techniques. Currently, there exists some alternatives for digitizing sticky notes, but none keep track of notes that have already been digitized, allowing for duplicate notes to be created when scanning multiple images of the same Kanban board. Kanban boards are widely used in various industries, and being able to recognize, and possibly in the future even digitize entire Kanban boards could provide users with extended functionality. The implementation presented in this thesis is able to, given two images, detect the Kanban boards in each image and rectify them. The rectified images are then sent to the Google Cloud Vision API for text detection. Then, the rectified images are used to detect all the sticky notes. The positional information of the notes and columns of the Kanban boards are then used to filter the text detection to find the text inside each note as well as the header text for each column. Between the two images, the columns are compared and matched, as well as notes of the same color. If columns or notes in one image do not have a match in the second image, it is concluded that the boards are different, and the user is informed of why. If all columns and notes in one image have matches in the second image but some notes have moved, the user is informed of which notes that have moved, and how they have moved as well. The different experiments conducted in this thesis on the implementation show that it works well, but it is very confined to strict requirements, making it unsuitable for commercial use. The biggest problem to solve is to make the implementation more general, i.e. the Kanban board layout, sticky note shapes and colors as well as their actual content.