Chart-Text: A Fully Automated Chart Image Descriptor
Abhijit Balaji, Thuvaarakkesh Ramanathan, Venkateshwarlu Sonathi

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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Image Retrieval and Classification Techniques · Image and Object Detection Techniques
