ChartParser: Automatic Chart Parsing for Print-Impaired

TL;DR

ChartParser is an automated system that extracts, classifies, and converts chart data from research papers into accessible formats for print-impaired users, improving scientific information accessibility.

Contribution

The paper introduces a fully automated pipeline combining deep learning, OCR, and image processing to extract and convert chart data into accessible formats for BLV individuals.

Findings

Effective extraction of bar charts from research papers.

Conversion of chart data into screen-reader friendly tables.

Successful application to real-world research paper charts.

Abstract

Infographics are often an integral component of scientific documents for reporting qualitative or quantitative findings as they make it much simpler to comprehend the underlying complex information. However, their interpretation continues to be a challenge for the blind, low-vision, and other print-impaired (BLV) individuals. In this paper, we propose ChartParser, a fully automated pipeline that leverages deep learning, OCR, and image processing techniques to extract all figures from a research paper, classify them into various chart categories (bar chart, line chart, etc.) and obtain relevant information from them, specifically bar charts (including horizontal, vertical, stacked horizontal and stacked vertical charts) which already have several exciting challenges. Finally, we present the retrieved content in a tabular format that is screen-reader friendly and accessible to the BLV users. We present a thorough evaluation of our approach by applying our pipeline to sample real-world annotated bar charts from research papers.