Visual Clarity Analysis and Improvement Support for Presentation Slides
Shinji Oyama, Toshihiko Yamasaki · 2019
Presentation slides offer effective ways to deliver information in various fields. It has become easier to create slides owing to advanced presentation software such as PowerPoint. However, novices still face difficulty in designing slides that are easily comprehensible, as few slide evaluation methods exist that can objectively judge the quality of slides. In this paper, we analyze the features extracted from slides and tackle a simple classification problem, i.e., whether the input single-page slide is easy to understand. For evaluation, we created a new dataset of 1,000 PowerPoint slides with visual clarity label by using a crowdsourcing service. Using the 30% of the slides with high/low clarity rating, we achieved an accurate classification rate of 90.3%. We further proposed a feedback system that supports the improvement of slide designs. User study demonstrates that our system, which uses feedback on visual clarity scores and areas that should be modified, effectively supports slide improvement.