Automated Tactilization of Graphical Images: Full Access to Math, Science, and Engineering for Blind Students

图形图像的自动触觉化：盲人学生全面接触数学、科学和工程

• 批准号: 0415273
• 负责人: Richard Ladner
• 金额: --
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0415273&HistoricalAwards=false
• 关键词: Automated; Tactilization; Graphical; Images; Full

Graphical images (line graphs, bar charts, diagrams, illustrations, etc.) are prevalent in math, science, and engineering (MSE) textbooks at all educational levels. But while studies have shown that tactual perception is the best modality for comprehension of graphical images by people who are visually impaired, the graphical images found in textbooks typically aren't available in this format. Visually impaired students' lack of full access to the contents of textbooks impedes their learning, development, and success in MSE careers, areas in which individuals with disabilities are underrepresented. This project seeks to address this problem, by developing innovative ways to overcome obstacles to the timely translation of graphical images into a tactual format. The needs of two user communities will be addressed: transcribers who translate graphical images into tactual formats within low- and high-production environments; and students who are in MSE classes at the K-12 and postsecondary education levels, are blind, and read Braille. To these ends, the PI has assembled an interdisciplinary team with expertise in image processing, machine learning, IR, HCI, experiment design, and addressing the needs of students with disabilities. The PI and his team will design and develop the Tactile Graphics Assistant (TG Assistant), a set of plug-ins for Adobe Photoshop and Illustrator, which will support transcribers in transforming, as automatically and intelligently as possible, graphical images into a high-quality tactual form that can be reproduced and then used by students who are blind. Empirical studies will be conducted to better understand the perception of tactile graphics, and to inform the design of prediction models to estimate image comprehension time and comprehension accuracy, the application of machine learning techniques to classify images by their type, and the design of image processing algorithms to carry out the steps (appropriate for the image type) to translate an image into a tactual form. A user-centered design approach will be followed during the development of the TG Assistant. Project benefits will be documented by three proof-of-concept activities, wherein the TG Assistant will be used to provide access to textbook images to three students at the K-8, high school, and postsecondary education levels. Broader Impact. Tactual access to graphical images will improve blind students' learning, performance, retention, and potential to succeed in MSE careers. Project outcomes will include invaluable data on the tactual perception of tactile graphics, new image processing and classification algorithms, and a usable tool to streamline the translation of graphical images into a tactual format. Research findings on the translation of graphical images will be summarized as a set of guidelines and distributed to transcribers. The PI will make an effort to incorporate the guidelines and TG Assistant into transcriber training programs. The research team includes individuals from under-represented groups (women, minority, and disabled individuals.

图形图像（折线图、条形图、图表、插图等）在所有教育级别的数学、科学和工程 (MSE) 教科书中普遍存在。 但是，尽管研究表明触觉感知是视障人士理解图形图像的最佳方式，但教科书中的图形图像通常不以这种格式提供。 视障学生无法充分接触课本内容，这阻碍了他们在 MSE 职业中的学习、发展和成功，而在这些领域，残疾人的代表性不足。 该项目旨在通过开发创新方法来克服将图形图像及时转换为触觉格式的障碍，从而解决这个问题。 将满足两个用户社区的需求：在低生产环境和高生产环境中将图形图像翻译成触觉格式的转录员； K-12 和高等教育阶段 MSE 课程的学生是盲人，并且阅读盲文。 为此，PI 组建了一支跨学科团队，拥有图像处理、机器学习、IR、HCI、实验设计和满足残疾学生需求方面的专业知识。 PI 和他的团队将设计和开发触觉图形助手（TG Assistant），这是一套适用于 Adob​​e Photoshop 和 Illustrator 的插件，它将支持转录员尽可能自动和智能地将图形图像转换为高清晰度的图像。盲人学生可以复制并使用的优质触觉形式。 将进行实证研究，以更好地理解触觉图形的感知，并为预测模型的设计提供信息以估计图像理解时间和理解准确性，应用机器学习技术按图像类型对图像进行分类，以及图像处理的设计执行将图像转换为触觉形式的步骤（适合图像类型）的算法。 TG Assistant的开发将遵循以用户为中心的设计理念。 项目效益将通过三项概念验证活动来记录，其中 TG Assistant 将用于为 K-8、高中和高等教育级别的三名学生提供课本图像的访问权限。 更广泛的影响。对图形图像的触觉访问将提高盲人学生的学习、表现、记忆力以及在 MSE 职业生涯中取得成功的潜力。 项目成果将包括有关触觉图形触觉感知的宝贵数据、新的图像处理和分类算法，以及简化图形图像到触觉格式转换的可用工具。 关于图形图像翻译的研究成果将被总结为一套指南并分发给转录者。 PI 将努力将指南和 TG Assistant 纳入转录员培训计划中。 研究团队包括来自代表性不足的群体（妇女、少数民族和残疾人）的个人。