Access to digital images is important to people who are blind or have low vision (BLV). Many contemporary image description efforts do not take into account this population’s nuanced image description preferences. In this paper, we present a qualitative study that provides insight into 28 BLV people’s experiences with descriptions of digital images from news websites, social networking sites/platforms, eCommerce websites, employment websites, online dating websites/platforms, productivity applications, and e-publications. Our findings reveal how image description preferences vary based on the source where digital images are encountered and the surrounding context. We provide recommendations for the development of next-generation image description technologies inspired by our empirical analysis.
Image captions, alt text, accessibility, visual impairment
Human-centered computing, Empirical studies inaccessibility
Digital images are plentiful across the media and information landscape. Towards enabling people who are blind or have low vision (BLV) to consume such content, a variety of efforts focus on the provision of alternative text (alt-text) that is read through a screen reader. A screen reader is a software application that enables people who are BLV to read the text that is displayed on the computer screen with a speech synthesizer or Braille display. Alt-text image descriptions are read off by a screen reader when a content author has followed recommended protocol, e.g. , and created an alt text attribute within a document or website’s source code.
Though the provision of alt text is a best practice, most digital images lack descriptions. A 2017 study of popular websites in many categories (as ranked by alexa.com) found that between 20% and 35% of images lacked descriptions and that many images that did contain alt text had extremely low-quality descriptions, such as the word “image” or a filename . Images on social media are particularly problematic; a 2018 study found that only 0.1% of images on Twitter had alt text . While the ideal is for content authors to always provide high-quality image descriptions (i.e. using the alt text field) at the time of document authorship, many are not despite efforts and resources developed to scaffold content authors in producing them (e.g., [13, 26]).
The absence of alt text from content authors has motivated scholars and practitioners to innovate, by introducing a variety of more scalable image description services that are powered by humans [4, 5, 7, 6, 45], computers [14, 24, 35, 37, 38, 43], and a mixture of their efforts [17, 28, 32, 33]. In designing image descriptions, such services can leverage the many guidelines for how to write effective descriptions [13, 11, 26, 29, 30, 34, 39, 41, 42, 44]. However, existing guidelines are limited in that they do not clarify how to account for the finding of Petrie et al.  in 2005 – an interview study with five blind people that found that the most useful information to be included “was thought to be context-dependent”, i.e. based on the source in which the image is found.
Towards the goal of closing this description gap between what people want and what is provided, we present a qualitative study designed to investigate the image description preferences of people who are BLV. We interviewed 28 BLV people, guided by the question: “What are BLV people’s experiences with and preferences for image descriptions found in different digital sources?”. We draw on the following definition of source: the platforms and media where one may encounter digital images. Examples of digital images found in different sources are shown in Figure 1.
We focused our investigation on seven sources: news websites, social networking sites/platforms, eCommerce websites, employment websites, online dating websites/platforms, productivity applications, and e-publications. We conclude with recommendations regarding what is important information to incorporate into image descriptions found in different sources. These recommendations can be of great value for improving human-powered, computer-powered, and hybrid image description services for people who are BLV. More generally, our work contributes to the design of social and technical infrastructures that are accessible to all and support people to engage more fully with digital media.
In this study, we took a holistic approach to examine BLV people’s experiences with digital images found on different sources and the variance of their description preferences across sources. The findings we present in this paper may be used by scholars and practitioners who are working to refine the ways in which image descriptions are generated by human-powered services, AI-powered services, and hybrid services for generating image descriptions. Ensuring image accessibility for people who are BLV is particularly important given the widespread proliferation of visual media. Such descriptions may also benefit sighted users, such as when accessing media eyes-free (i.e., via a voice agent such as Alexa or Cortana), and by providing additional metadata that can support information retrieval. Developing and evaluating source-dependent image descriptions based on the guidelines presented herein is a promising area for future study.
The Kavian Scientific Research Association (KSRA) is a non-profit research organization to provide research / educational services in December 2013. The members of the community had formed a virtual group on the Viber social network. The core of the Kavian Scientific Association was formed with these members as founders. These individuals, led by Professor Siavosh Kaviani, decided to launch a scientific / research association with an emphasis on education.
KSRA research association, as a non-profit research firm, is committed to providing research services in the field of knowledge. The main beneficiaries of this association are public or private knowledge-based companies, students, researchers, researchers, professors, universities, and industrial and semi-industrial centers around the world.
Our main services Based on Education for all Spectrum people in the world. We want to make an integration between researches and educations. We believe education is the main right of Human beings. So our services should be concentrated on inclusive education.
The KSRA team partners with local under-served communities around the world to improve the access to and quality of knowledge based on education, amplify and augment learning programs where they exist, and create new opportunities for e-learning where traditional education systems are lacking or non-existent.
FULL Paper PDF file:imagedesc_chi2020
“Person, Shoes, Tree. Is the Person Naked?” What People with Vision Impairments Want in Image Descriptions
CHI ’20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PDF reference and original file: Click here
Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.