Information Organization with Infographics using Machine Learning — A Survey
By: Anjul Tyagi
Tags: Infographics, Data Science, Data Visualization, Machine Learning, Deep Learning

Text-Based Automation [5]

Automatic infographics generated for the statement: More than 20% of Smartphone users are social network users. [5]

Another system in this series is known as the Text-to-Viz. Given a statistical statement, this system tries to directly come up with complete infographic design. Unlike other tools for infographic management, where the user needs to/can edit the final design of the infographic, Text-to-Viz generates these well defined, aesthetic infographics which need no editing. The best use case of this system if for the scenarios where the user doesn’t need to create a very design rich infographic but needs something simple and quick to present a piece of statistical information in a better way. According to this paper, there are 4 types of most common infographics:

Infographics for each category with their occurrence percentage. [5]

Statistical-based: Infographics containing charts, pictographs, etc. for presenting statistical information.

Timeline-based: Presenting timeline information.

Process-based: Step by step action presentation.

Location-based: Showing information on a map.

Since, according to this research, around 50% of the infographics are statistical-based, and in that, around 45% are proportion-based, they only tried to create an automatic infographic generation system for this set of infographics. After that, the next step was to study different parts of the proportion-based information text. An example is shown below where they are trying to classify and extract pieces of information to be designed separately.

Classifying different parts of the proportion-based information. [5]

Next up, the design space needs to be separated based on where different elements are to be placed. The researchers came up with 20 template designs where different elements could be placed based on the rules mentioned in the paper.

Designing the infographic from a template. [5]

Rules that were considered while designing the templates. [5]

Image Chart Fusion Automation [6]

Images with embedded charts. [6]

The last techniques in the list of automatic infographics generation are the techniques to design images containing chats, as shown in the above image. A survey on the photographic infographics showed the type of charts that are frequently used to present data embedded inside images [6]. These are Bar charts [41.2%], Pie charts [21.4%], Line charts [9.4%], and Scatterplots [2.2%]. Other than the charts, other ways of embedding this information are Single Divided Object: where the graphics are divided into smaller parts along a horizontal/vertical axis and the area of these divisions can be based on the ratio of different quantities we are trying to compare. This is followed by Multiple Resized Objects where the objects inside an image are sized according to the data they are trying to portray. Using the information about how and where the information is represented, researchers generally follow the pipeline shown below to generate final infographics.

The workflow of embedding charts into images. [6]

So, from a given dataset, relevant variables are selected and the images corresponding to those variables are collected. When the user selects one of these images, then the charts are generated for the selected variables. These are to be embedded inside the selected images. At this stage, the user can either drag an area on the image to embed the chart on, or they can choose features from that image (for eg. Hough lines) to use as an anchor to overlay charts on these images.

Chart embedding with the masking technique. [6]

Chart embedding based on image features (Hough lines in this case). [6]

Overall, it is reasonable to represent “Trends/Timeline Data (Line Charts)” with Hugh Lines and “Pie Charts/Bar Charts, etc. ” with a masking technique. To fine-tune these embeddings, there are different types of distortions that can be calculated for each type of chart. For example, comparing the slope of the high lines and the line chart can give an estimated distortion of how well the line chart is embedded in the image. These values are used to optimize the fit of the charts on the images to generate aesthetic info-images. And finally, all of this is implemented in an interface where the users can use their domain knowledge or designing skills to fine-tune these automatically generated results.

Conclusion

We discussed methods for generating infographics on different types of datasets: Timeline, Icons, Text, and Charts. All of these methods focus on a certain aspect of infographics focusing on the type of data they are trying to represent. These cues are generally an outcome of a survey of already existing infographics and then use that information to automate the process. This is still a new research area with a very promising future. The future direction of research can be to explore more variety of infographics and then combine the existing techniques with the new techniques to create a more holistic, generalized technique to automate/semi-automate this tedious process of infographics generation.

References

[1] Towards Automated Infographic Design: Deep Learning-based Auto-Extraction of Extensible Timeline, Zhutian Chen et. al, 2019

[2] Timeline Storyteller, Matthew Brehmer et. al, 2019

[3] ICONATE: Automatic Compound Icon Generation and Ideation, Nanxuan Zhao et. al, 2020

[4] Exploring Visual Information Flows in Infographics, Min. Lu. et. al, 2020

[5] Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements. Weiwei Cui et. al., 2019

[6] Infomages: Embedding Data into Thematic Images, Darius Coelho et. al., 2020