Data visualization is a crucial tool to facilitate human beings comprehending and analyzing the data better. Graphical representation is the simplest and most common way of data representation among all the techniques.

In sectors like medical, marketing, finance and others, where a plethora of numeric data is generated in real-time, graphical representation is an invaluable analysis tool. Visualizing data trends helps summarize and visualize the available trends. However, when plotting categories of data using unique colors, capturing and highlighting the intersection of these lines are complex tasks, given that the color values get changed. For example, when blue and yellow intersect, the color at the intersection changes, resulting in a break in traceability and making it appear as two different representations even though it is a single dataset representation. Overcoming this challenge requires automated techniques to recognize intersecting lines as a single representation. This kind of challenge can be overcome by evolving AI techniques to successfully differentiate and highlight these automatically. This solution provides an automated solution to the problem, which not only assists the system in identifying the data values that a tester requires but also helps segregate it from the rest of the graph, reducing the time of manual methods.

This whitepaper proposes a solution using Computer Vision (CV) and Deep Learning (DL) techniques. Both techniques are fields of AI: DL trains a computer to think and work like a human, while CV deals with digital images and videos to comprehend information the system can utilize to derive an outcome. They both facilitate task automation.