吉林大学学报(理学版) ›› 2024, Vol. 62 ›› Issue (3): 697-703.

• • 上一篇    下一篇

基于WAI-ARIA的网页导航栏地标属性的标识方法

李玉聪1, 汪士钦2, 张梦玺2, 刘华虓2   

  1. 1. 吉林大学第一医院, 长春 130021; 2. 吉林大学 计算机科学与技术学院, 长春 130012
  • 收稿日期:2023-08-04 出版日期:2024-05-26 发布日期:2024-05-26
  • 通讯作者: 刘华虓 E-mail:liuhuaxiao@jlu.edu.cn

Landmark Attribute Identification Method of Webpage Navigation Bar Based on WAI-ARIA

LI Yucong1, WANG Shiqin2, ZHANG Mengxi2, LIU Huaxiao2   

  1. 1. The First Hospital of Jilin University, Changchun 130021, China; 2. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2023-08-04 Online:2024-05-26 Published:2024-05-26

摘要: 针对多样化网页上视障用户导航的难题, 提出一种自动标识导航栏地标的方法, 以提高网页无障碍性. 首先, 通过设计启发式规则, 根据导航栏内有序元素排列以及子元素内常含超链接和精炼文字等规则, 自动提取导航栏内的元素; 其次, 采用决策树二分类算法, 用于分类导航栏中特征差异显著的元素; 最后, 对已识别的导航栏元素进行地标属性注入. 在对100个网站的实验评估中, 该方法成功识别了92.6%的导航栏元素, 而注入的地标属性则显著提升了网站的无障碍性能, 从而改善了视障用户的使用体验.

关键词: 网页无障碍, 地标, 导航栏识别, 决策树算法

Abstract: Aiming at the problem of  the navigational challenges for visually impaired users on diverse webpages, we proposed a method for automatically identifying navigation bars to improve  webpage accessibility. Firstly, by designing heuristic rules, elements within the navigation bars were  autonomously extracted based on the ordered element arrangement within the navigation bar, as well as rules such as hyperlinks and succinct textual content within sub-elements. Secondly, a decision tree binary classification algorithm was used to categorize elements with pronounced feature disparities in the navigation bars. Finally, the identified navigation bar elements were subject to the injection of Landmark attributes. In experimental evaluations of  100 websites, the method successfully identified  92.6% of navigation bar elements, and the infusion of Landmark attributes significantly improves website accessibility, thereby ameliorating the user experience for visually impaired individuals.

Key words: webpage accessibility, Landmark, navigation bar recognition, decision tree algorithm

中图分类号: 

  • TP311.5