Tensor Fields for Data Extraction from Chart Images: Bar Charts and Scatter Plots

TL;DR

This paper introduces a novel tensor field model for automating data extraction from bar charts and scatter plots in chart images, demonstrating tensor voting's effectiveness in this task.

Contribution

It proposes using positive semidefinite second-order tensor fields as a new model for chart image data extraction, a first in this context.

Findings

Tensor voting effectively extracts data from bar charts and scatter plots.

The tensor field model successfully identifies degenerate points for data extraction.

Histograms are effectively handled as a special case of bar charts.

Abstract

Charts are an essential part of both graphicacy (graphical literacy), and statistical literacy. As chart understanding has become increasingly relevant in data science, automating chart analysis by processing raster images of the charts has become a significant problem. Automated chart reading involves data extraction and contextual understanding of the data from chart images. In this paper, we perform the first step of determining the computational model of chart images for data extraction for selected chart types, namely, bar charts, and scatter plots. We demonstrate the use of positive semidefinite second-order tensor fields as an effective model. We identify an appropriate tensor field as the model and propose a methodology for the use of its degenerate point extraction for data extraction from chart images. Our results show that tensor voting is effective for data extraction from bar charts and scatter plots, and histograms, as a special case of bar charts.