Advancing Visual Specification of Code Requirements for Graphs

TL;DR

This paper introduces a hybrid machine learning approach that enables users to visually specify code requirements for data visualizations, making it easier for humanities researchers to create meaningful visual data representations.

Contribution

It presents a novel hybrid model combining neural networks and OCR to translate visual specifications into code for data visualization.

Findings

Improved ease of creating visualizations for non-programmers.

Enhanced accuracy in translating visual specifications to code.

Facilitated data exploration for humanities researchers.

Abstract

Researchers in the humanities are among the many who are now exploring the world of big data. They have begun to use programming languages like Python or R and their corresponding libraries to manipulate large data sets and discover brand new insights. One of the major hurdles that still exists is incorporating visualizations of this data into their projects. Visualization libraries can be difficult to learn how to use, even for those with formal training. Yet these visualizations are crucial for recognizing themes and communicating results to not only other researchers, but also the general public. This paper focuses on producing meaningful visualizations of data using machine learning. We allow the user to visually specify their code requirements in order to lower the barrier for humanities researchers to learn how to program visualizations. We use a hybrid model, combining a neural network and optical character recognition to generate the code to create the visualization.