Chart-Text: A Fully Automated Chart Image Descriptor

TL;DR

This paper introduces Chart-Text, an automated system that generates textual descriptions of chart images to improve accessibility for visually impaired users, achieving high classification and extraction accuracy.

Contribution

The paper presents a novel fully automated system for classifying chart types and extracting textual data to generate descriptions, enhancing web accessibility.

Findings

99.72% accuracy in chart classification

78.9% accuracy in data extraction and description generation

Effective for aiding visually impaired users

Abstract

Images greatly help in understanding, interpreting and visualizing data. Adding textual description to images is the first and foremost principle of web accessibility. Visually impaired users using screen readers will use these textual descriptions to get better understanding of images present in digital contents. In this paper, we propose Chart-Text a novel fully automated system that creates textual description of chart images. Given a PNG image of a chart, our Chart-Text system creates a complete textual description of it. First, the system classifies the type of chart and then it detects and classifies the labels and texts in the charts. Finally, it uses specific image processing algorithms to extract relevant information from the chart images. Our proposed system achieves an accuracy of 99.72% in classifying the charts and an accuracy of 78.9% in extracting the data and creating the corresponding textual description.