# Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline

## TL;DR

This paper presents a deep learning-based method for automatically extracting extensible timeline templates from bitmap infographics, facilitating automated infographic design for both experts and non-experts.

## Contribution

It introduces an end-to-end approach combining multi-task neural networks and a reconstruction pipeline to extract timeline templates from images, a novel solution for infographic automation.

## Key findings

- Effective extraction of timeline templates demonstrated on synthesized and real-world datasets.
- Quantitative evaluation shows high accuracy in parsing timeline features.
- Qualitative results confirm the approach's ability to generate useful templates from diverse infographics.

## Abstract

Designers need to consider not only perceptual effectiveness but also visual styles when creating an infographic. This process can be difficult and time consuming for professional designers, not to mention non-expert users, leading to the demand for automated infographics design. As a first step, we focus on timeline infographics, which have been widely used for centuries. We contribute an end-to-end approach that automatically extracts an extensible timeline template from a bitmap image. Our approach adopts a deconstruction and reconstruction paradigm. At the deconstruction stage, we propose a multi-task deep neural network that simultaneously parses two kinds of information from a bitmap timeline: 1) the global information, i.e., the representation, scale, layout, and orientation of the timeline, and 2) the local information, i.e., the location, category, and pixels of each visual element on the timeline. At the reconstruction stage, we propose a pipeline with three techniques, i.e., Non-Maximum Merging, Redundancy Recover, and DL GrabCut, to extract an extensible template from the infographic, by utilizing the deconstruction results. To evaluate the effectiveness of our approach, we synthesize a timeline dataset (4296 images) and collect a real-world timeline dataset (393 images) from the Internet. We first report quantitative evaluation results of our approach over the two datasets. Then, we present examples of automatically extracted templates and timelines automatically generated based on these templates to qualitatively demonstrate the performance. The results confirm that our approach can effectively extract extensible templates from real-world timeline infographics.