SBIR Phase I: Making Web Applications Accessible

该项目的更广泛的影响/商业潜力包括开发一种新的辅助技术，使视力受损的人可以访问动态web应用程序。这项被提议的技术将使盲人能够使用非常流行的网络应用程序，如社交网络、电子邮件、网上银行和旅行，使用起来和正常人一样方便。由此带来的屏幕阅读器用户生产力的提高将会改善受教育和就业的机会。这项拟议中的技术将作为屏幕阅读器应用程序的一部分进行商业化，该应用程序适用于视力受损的人群，美国有多达2500万人，全球有25万人。所提出的创新也可用于实现web自动化和web爬行的主流技术。这个小企业创新研究（SBIR）第一阶段的项目将调查使盲人可以访问动态web应用程序中的小部件的可行性。现代web应用程序的动态性已经成为使用屏幕阅读器访问web的盲人的一个严重障碍。不幸的是，虽然屏幕阅读器通常可以描述网页中所有单独的HTML元素，但它们不能识别小部件，小部件是组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。web应用程序中小部件的不可访问性会使整个web应用程序无法使用。该项目将开发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。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合行为与组件拼接在一起。生成的模型将被整合到一个商业屏幕阅读器中。

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.