论文标题
ChartParser：自动图表解析印刷障碍
ChartParser: Automatic Chart Parsing for Print-Impaired
论文作者
论文摘要
信息图表通常是科学文档的组成部分，用于报告定性或定量发现，因为它们使理解基本复杂信息变得更加简单。但是，他们的解释仍然是盲目，低视觉和其他印刷障碍（BLV）的人的挑战。在本文中，我们提出了ChartParser，这是一条完全自动化的管道，利用深度学习，OCR和图像处理技术从研究论文中提取所有图，将它们分为各种图表类别（条形图，线路图等），并从中获取相关信息，包括水平，vertical，Vertical，Vertical，Vertical，Vertical，Vertical，Vertical，堆叠的水平和堆叠式图表），这已经对其进行了一些挑战。最后，我们以屏幕阅读器友好且可供BLV用户访问的表格格式介绍所检索的内容。我们通过将管道应用于研究论文中的真实世界注释的条形图，对我们的方法进行了彻底的评估。
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.
