SandSlide: Automatic Slideshow Normalization

  • Authors: Sieben Bocklandt, Gust Verbruggen, Thomas Winters
  • Publication Date: 2021-09-05
  • Publication Venue: Proceedings of the 2021 16th International Conference on Document Analysis and Recognition (ICDAR)
  • Abstract: Slideshows are a popular tool for presenting information in a structured and attractive manner. There exists a wide range of different slideshows editors, often with their own proprietary encoding that is incompatible with other editors. Merging slideshows from different editors and making the slide design consistent is a nontrivial and time-intensive task. We introduce SandSlide, the first system for automatically normalizing a deck of slides from a PDF file into an editable PowerPoint file that adheres to the default slide templates, and is thus able to fully leverage the flexible layout capabilities of modern slideshow editors. SandSlide achieves this by labeling objects, using a qualitative representation to find the most similar slide layout and aligning content from the slide with this layout. To evaluate SandSlide, we collected and annotated slides from different slideshows. Our experiments show that a greedy search is able to obtain high responsiveness on supported and almost supported slides, and that a significant majority of slides fall into this category. Additionally, our annotated dataset contains fine-grained annotations on different properties of slideshows to further incentivize research on all aspects of the problem of slide normalization.
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Bocklandt, S., Verbruggen, G., & Winters, T. (2021). SandSlide: Automatic Slideshow Normalization. Document Analysis and Recognition – ICDAR 2021, 445–461.


Bocklandt, S., Verbruggen, G. and Winters, T. (2021) “SandSlide: Automatic Slideshow Normalization,” in Document Analysis and Recognition – ICDAR 2021. Cham: Springer International Publishing, pp. 445–461.


Bocklandt S, Verbruggen G, Winters T. SandSlide: Automatic Slideshow Normalization. In: Document Analysis and Recognition – ICDAR 2021. Cham: Springer International Publishing; 2021. p. 445–61.


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