The majority of scientific papers are distributed in PDF, which pose challenges for accessibility, especially for blind and low vision (BLV) readers. We characterize the scope of this problem by assessing the accessibility of 11,397 PDFs published 2010--2019 sampled across various fields of study, finding that only 2.4% of these PDFs satisfy all of our defined accessibility criteria. We introduce the SciA11y system to offset some of the issues around inaccessibility. SciA11y incorporates several machine learning models to extract the content of scientific PDFs and render this content as accessible HTML, with added novel navigational features to support screen reader users. An intrinsic evaluation of extraction quality indicates that the majority of HTML renders (87%) produced by our system have no or only some readability issues. We perform a qualitative user study to understand the needs of BLV researchers when reading papers, and to assess whether the SciA11y system could address these needs. We summarize our user study findings into a set of five design recommendations for accessible scientific reader systems. User response to SciA11y was positive, with all users saying they would be likely to use the system in the future, and some stating that the system, if available, would become their primary workflow. We successfully produce HTML renders for over 12M papers, of which an open access subset of 1.5M are available for browsing at https://scia11y.org/
翻译:大部分科学论文都在PDF中分发,这给无障碍环境,特别是盲人和低视力读者(BLV)带来挑战。我们通过评估在各种研究领域抽样的2010-2019年出版的11 397个PDF文件的无障碍范围来描述这一问题的范围,发现这些PDF中只有2.4%的PDF符合我们定义的所有无障碍标准。我们引入了SciA11y系统,以抵消无法获取性方面的一些问题。SciA11y 将若干机器学习模型用于提取科学PDF的内容,并将这一内容作为可访问的 HTML,并增加新的导航功能以支持屏幕读者用户。对提取质量的内在评估表明,我们系统制作的大多数 HTML(87%)的无障碍环境没有或只有某些可读性问题。我们进行了质量用户研究,以了解BLV研究人员在阅读论文时的需要,并评估SciA11y系统能否满足这些需要。我们将用户的研究结果归纳成一套供无障碍科学阅读系统的5个开放式设计建议。SciA11y的用户反应是积极的,所有用户都表示他们有可能在未来使用一个系统。