SBIR Phase I: Making Web Applications Accessible

该项目更广泛的影响/商业潜力包括开发一种新的辅助技术，使视力障碍者可以使用动态网络应用程序。这项拟议的技术将使盲人能够像有视力的人一样，使用非常流行的网络应用程序进行社交网络、电子邮件、网上银行和旅行。由此产生的屏幕阅读器用户的更高生产率将导致更多人获得教育和就业机会。这项拟议中的技术将作为针对视力障碍者的屏幕阅读器应用程序的一部分进行商业化，美国人口多达2500万，全球人口为25万。这项拟议的创新还可以用于实现网络自动化和网络爬行的主流技术。这个小型企业创新研究(SBIR)第一阶段项目将研究使动态网络应用中的小部件对盲人可用的可行性。现代Web应用程序的动态特性已经成为使用屏幕阅读器访问Web的盲人的严重障碍。不幸的是，虽然屏幕阅读器通常可以叙述网页中的所有单独的HTML元素，但他们不识别微件，微件是形成用户可以与之交互的离散用户界面对象的HTML元素的集合，例如，HTML窗口、下拉菜单、日历、建议框等。在Web应用中微件的不可访问性可能会使整个Web应用不可用。这个项目将开发Widget可访问性模型，该模型将使用机器学习技术来识别和分类网页中的动态Widget，并为它们提供通用的可访问接口。该模型将简洁地表示各种复杂程度不同的小部件，作为与(可继承的)属性和交互行为相关联的对象。WAM将驱动对Web应用程序中使用的小部件的自动检测，并在运行时将它们的组件组合在一起。由此产生的模型将被整合到商业屏幕阅读器中。

The broader impact/commercial potential of this project include the development of a novel assistive technology that will make dynamic web applications accessible for people with vision impairments. The proposed technology will empower blind people to utilize the immensely popular web applications for social networking, email, online banking, and travel with the same ease-of-use as is experienced by sighted people. The resulting higher productivity of screen-reader users will lead to improved access to education and employment. The proposed technology will be commercialized as part of a screen-reader application for people with vision impairments, a population of up to 25 Million people in the U.S. and a quarter of a million worldwide. The proposed innovation can also be utilized in mainstream technologies that enable web automation and web crawling.This Small Business Innovation Research (SBIR) Phase I project will investigate the feasibility of making widgets in dynamic web application accessible for blind people. The dynamic nature of modern day web applications has become a serious barrier for blind people who use screen readers to access the Web. Unfortunately, while screen readers can usually narrate all the individual HTML elements in a webpage, they do not recognize widgets, which are a collection of HTML elements forming discrete user interface objects with which the user can interact, e.g., HTML windows, dropdown menus, calendars, suggestion boxes, etc. Inaccessibility of widgets in a web application can make the entire web applications unusable. This project will develop the Widget Accessibility Model that will employ machine learning techniques to identify and classify dynamic widgets in web pages and supply them with generic accessible interfaces. The Model will succinctly represent an assortment of widgets, of varying degrees of complexity, as objects associated with (inheritable) properties and interaction behaviors. WAM will drive the automatic detection of widgets used in a web application and stitch together their composite behavior from their components at runtime. The resulting Model will be incorporated into a commercial screen reader.