1. A pictogram, also called an icon, is an ideogram that conveys its meaning through its pictorial resemblance. For my thesis, I would like to design a search engine that effectively organizes pictograms by their visual similarities. Its goal is to help both viewers and producers of a visual system by maintaining consistency in pictograms.
  2. Pictograms vary in their level of details, scale, weight, and style depending on their themes and purposes. However, at least inside one system (i.e. a mobile application), they should align together; else they become mere doodles than a language by lack of consistency. It's a special subject to me, because I've been studying and working as a graphic designer and seen many users or designers getting confused about the subject. Providing clear way to categorize icons not only benefits the workflow of designers/producers, but also helps the viewers of that designed output, such as a presentation or a website.
  3. The final output will be a prototype of a web based search engine involving the Noun Project API, which has the largest icon database.


  1. My thesis is heavily influenced by machine learning projects as Font Map or Quick, Draw! -- which both build relationship between visual elements by analyzing their patterns. Applying deep learning techniques to icons also has been done before (brandmark.io/intro/), but only for the purpose of "logo maker". It's great that I have many similar projects, so my research started from studying them.
  2. After studying the existing examples, I've learned that it is possible to sort pictograms. To start, I needed a large source of icons and I decided to use the Noun Project API, attracted by its high number of collection. Hence, my pictogram searching engine will be based of the Noun Project. The deep learning process is not only for designing the final search tool, but also to not arbitrarily pick icons for my research and user testing.
  3. During the process, I've already started conversation with some graphic designers about how they feel about the current method of searching icons. I'm also taking courses from Gene Kogan and Yining Shi, who are both experts in machine learning -- so I'll reach out to them for technical advice.
  4. George, Alison. “Code Hidden in Stone Age Art May Be the Root of Human Writing.” New Scientist, New Scientist, 2016, www.newscientist.com/article/mg23230990-700-in-search-of-the-very-first-coded-symbols.
    Ho, Kevin. “Organizing the World of Fonts with AI – IDEO Stories – Medium.” Medium.com, Medium, 20 Apr. 2017, medium.com/ideo-stories/organizing-the-world-of-fonts-with-ai-7d9e49ff2b25.
    Drucker, Johanna. The Visible Word: Experimental Typography and Modern Art. University of Chicago Press, 1997.
    “Branded in Memory.” Signs.comwww.signs.com/branded-in-memory.
    “Brandmark - the Smart Logo Maker.” Brandmark Logo Maker - the Most Advanced AI Logo Design Tool, brandmark.io/intro/.
  5. Schedule:
    • Until Feb 15: concept development, brief technical testing
    • Feb 16 ~ March 5: extract visual patterns from the Noun Project, user test of the 'audiences' who view icons
    • March 6 ~ April 16: visualize the analysis, start designing the prototype of interface based on the previous results, user testing of the 'producers' who search and include icons in their work
    • April 17 ~ May 6: finish prototype design and make presentation
  6. The part that still needs to be found out is the portion of actual programming in prototype. While the final prototype will be built with Sketch toolkit to present my idea as clear as possible, its basic interface will be based of some working code. This is something that will be continuously adjusted throughout the research.


  1. I have relatively little amount of knowledge about machine learning, and it's the core part for my research. To improve my understanding, I'm taking two other ML related courses. It's not that I'm trying to build a whole new type of convolutional net, and as I mentioned, there are many similar examples that already exist. My thesis is more centered around how to apply the existing technology to improve the Noun Project service, than inventing a new one. In case of technical difficulties, I'm planning to ask for advice from Gene and Yining, while I'm already gaining a lot of resources from Gene's course.
  2. My thesis is composed of two big parts of analysis and prototype. The analysis part is pretty straightforward: I extract visual similarities from the Noun Project icons, and test if the viewers have easier time with the icon set in consistency, compared to the one without it. There are bit more unknowns in the prototype part, regarding how much programming will be actually done as a final output.
  3. The analysis will be visualized with at least two types of different icons, thus it can show the relationship between multiple terms. The prototype lets the users to pick multiple terms and get a set of icons with similarity, or change them altogether simultaneously.