Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to detect salient information, perceive emotional cues, and comprehend content authors’ intents in discussions. Many described experiencing mental fatigue when listening to ‘flat’, ‘uninspiring’ voices, noting that their attention tended to diminish quickly over time. The participants also indicated that they often tried to ‘add’ prosodic variation or emotional inflection themselves in their minds, but characterized this compensatory effort as mentally taxing and cognitively demanding. To address this issue, we introduce VoxVista, a multi-voice design framework driven by a large language model that leverages a custom voice-preference dataset to assign personalized voice profiles to user posts in discussions, thereby replacing the traditional monotone narration in screen readers with a more expressive, dynamic, and contextually-aware narration. In a study with 20 blind participants, we observed that VoxVista significantly improved user engagement, comprehension, and willingness to continue listening to longer discussions.

While online shopping platforms provide convenience and autonomy to blind users, their non-visual interactions remain underexplored at a micro-behavioral level. Existing studies have primarily emphasized accessibility and usability challenges but have overlooked how fine-grained, screen reader-driven keystroke-level behaviors reflect users’ cognitive strategies. In this paper, we present the findings of a longitudinal study with 25 blind participants to examine their micro-behavioral patterns, using keyboard activity and screen reader logs on both familiar and unfamiliar e-commerce websites. We complemented this study with semi-structured interviews to contextualize the uncovered micro-behavioral patterns. Our results revealed patterns in how blind users draw upon cognitive maps and well-established shortcut routines developed on familiar websites to streamline navigation on unfamiliar platforms. However, unfamiliar websites, even when structurally accessible, often introduced elevated navigation entropy, increased shortcut failures, and induced more exploratory behavior, as users worked to reconstruct new mental models. Additionally, we also identified a strong preferential structure in keyboard shortcut use, where users maintain a personalized and often chronologically-ranked sequence of keystrokes. Furthermore, most users approached shopping with pre-planned objectives, relying on targeted search queries rather than broad ad-hoc product exploration for securing the ‘best deals’. Based on the study insights, we discuss design considerations for assistive technology developers and e-commerce websites to further improve the online shopping experience for blind users.

The growing demand for computer professionals, driven by the expanding Information Technology industry, has led to numerous inclusive computing education efforts. These efforts have even included blind or visually-impaired (BVI) students, who are being increasingly encouraged to pursue education and a career in computing, despite the visually-oriented nature of the discipline. Extant literature has predominantly focused on identifying and addressing the accessibility barriers faced by BVI students to promote more inclusive learning environments. While few studies have also investigated the accessibility of computing education from the perspectives of BVI learners and instructors, these have been primarily situated in the Global North contexts; there is still a knowledge gap regarding the teaching and learning experiences of instructors and BVI students, respectively, in resource-constrained Global South contexts, where accessibility awareness and inclusion efforts are at nascent stages. To fill this gap, we conducted an interview study with 15 participants in India, where we inquired with BVI students, instructors, and BVI professionals, regarding their challenges, experiences, and needs pertaining to computing education. The study revealed that BVI students face significant difficulty in comprehending the instructional materials, the instructors often deal with courses not progressing as planned despite meticulous preparation, the students heavily depend on peer learning for grasping computing concepts, and they need additional support for managing the cognitively-burdensome task of simultaneously learning computing concepts and screen readers. Informed by the findings, we offer recommendations to improve computing curricula for BVI students and discuss self-learning assistive tools to supplement accessible computing education.

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a multimodal LLM (e.g., recognition of common meme templates and cultural references) to convey intent, timing, and implicit meaning through conversational interaction. We evaluate MemeBuddy in a user study with 14 blind participants. Results show that dialog-style meme representations consistently improve engagement and user satisfaction compared to caption-style descriptions, while maintaining comparable comprehension.

Significant changes in the digital employment landscape, driven by rapid technological advancements and the COVID-19 pandemic, have introduced new opportunities for blind and visually impaired (BVI) individuals in developing countries like India. However, a significant portion of the BVI population in India remains unemployed despite extensive accessibility advancements and job search interventions. Therefore, we conducted semi-structured interviews with 20 BVI persons who were either pursuing or recently sought employment in the digital industry. Our findings reveal that despite gaining digital literacy and extensive training, BVI individuals struggle to meet industry requirements for fulfilling job openings. While they engage in self-reflection to identify shortcomings in their approach and skills, they lack constructive feedback from peers and recruiters. Moreover, the numerous job intervention tools are limited in their ability to meet the unique needs of BVI job seekers. Our results, therefore, provide key insights that inform the design of future collaborative intervention systems that offer personalized feedback for BVI individuals, effectively guiding their self-reflection process and subsequent job search behaviors, and potentially leading to improved employment outcomes.

The importance of data charts is self-evident, given their ability to express complex data in a simple format that facilitates quick and easy comparisons, analysis, and consumption. However, the inherent visual nature of the charts creates barriers for people with visual impairments to reap the associated benefits to the same extent as their sighted peers. While extant research has predominantly focused on understanding and addressing these barriers for blind screen reader users, the needs of low-vision screen magnifier users have been largely overlooked. In an interview study, almost all low-vision participants stated that it was challenging to interact with data charts on small screen devices such as smartphones and tablets, even though they could technically "see" the chart content. They ascribed these challenges mainly to the magnification-induced loss of visual context that connected data points with each other and also with chart annotations, e.g., axis values. In this paper, we present a method that addresses this problem by automatically transforming charts that are typically non-interactive images into personalizable interactive charts which allow selective viewing of desired data points and preserve visual context as much as possible under screen enlargement. We evaluated our method in a usability study with 26 low-vision participants, who all performed a set of representative chart-related tasks under different study conditions. In the study, we observed that our method significantly improved the usability of charts over both the status quo screen magnifier and a state-of-the-art space compaction-based solution.

Bar charts are widely used for their simplicity in data representation, prompting numerous studies to explore and model how users interact with and perceive bar chart information. However, these studies have predominantly focused on sighted users, with a few also targeting blind screen-reader users, whereas the graphical perception of low-vision screen magnifier users is still an uncharted research territory. We fill this knowledge gap in this paper by designing four experiments for a laboratory study with 25 low-vision participants to examine their graphical perception while interacting with bar charts. For our investigation, we built a custom screen magnifier-based logger that captured micro-interaction details such as zooming and panning. Our findings indicate that low-vision users invest significant time counteracting blurring and contrast effects when analyzing charts. We also observed that low-vision users struggle more in interpreting bars within a single-column stack compared to other stacked bar configurations, and moreover, for a few participants, the perception accuracy is lower when comparing separated bars than when comparing adjacent bars.

There have been many studies on understanding data visualizations regarding general users. However, we have a limited understanding of how people with ADHD comprehend data visualizations and how it might be different from the general users. To understand accessible data visualization for people with ADHD, we conducted a crowd-sourced survey involving 70 participants with ADHD and 77 participants without ADHD. Specifically, we tested the chart components of color, text amount, and use of visual embellishments/pictographs, finding that some of these components and ADHD affected participants’ response times and accuracy. We outlined the neurological traits of ADHD and discussed specific findings on accessible data visualizations for people with ADHD. We found that various chart embellishment types affected accuracy and response times for those with ADHD differently depending on the types of questions. Based on these results, we suggest visual design recommendations to make accessible data visualizations for people with ADHD.

Data chart visualizations and text are often paired in news articles, online blogs, and academic publications to present complex data. While chart visualizations offer graphical summaries of the data, the accompanying text provides essential context and explanation. Associating information from text and charts is straightforward for sighted users but presents significant challenges for individuals with low vision, especially on small-screen devices such as smartphones. The visual nature of charts coupled with the layout of the text inherently makes it difficult for low vision users to mentally associate chart data with text and comprehend the content due to their dependence on screen magnifier assistive technology, which only displays a small portion of the screen at any instant due to content enlargement. To address this problem, in this paper, we present a smartphone-based multimodal mixed-initiative interface that transforms static data charts and the accompanying text into an interactive slide show featuring frames containing “magnified views” of relevant data point combinations. The interface also includes a narration component that delivers tailored information for each “magnified view”. The design of our interface was informed by a user study with 10 low-vision participants, aimed at uncovering low vision interaction challenges and user-interface requirements with multimodal documents that integrate text and chart visualizations. Our interface was also evaluated in a subsequent study with 12 low-vision participants, where we observed significant improvements in chart usability compared to both status-quo screen magnifiers and a state-of-the-art solution.

Perusing web data items such as shopping products is a core online user activity. To prevent information overload, the content associated with data items is typically dispersed across multiple webpage sections over multiple web pages. However, such content distribution manifests an unintended side effect of significantly increasing the interaction burden for blind users, since navigating to-and-fro between different sections in different pages is tedious and cumbersome with their screen readers. While existing works have proposed methods for the context of a single webpage, solutions enabling usable access to content distributed across multiple webpages are few and far between. In this paper, we present InstaFetch, a browser extension that dynamically generates an alternative screen reader-friendly user interface in real-time, which blind users can leverage to almost instantly access different item-related information such as description, full specification, and user reviews, all in one place, without having to tediously navigate to different sections in different webpages. Moreover, InstaFetch also supports natural language queries about any item, a feature blind users can exploit to quickly obtain desired information, thereby avoiding manually trudging through reams of text. In a study with 14 blind users, we observed that the participants needed significantly lesser time to peruse data items with InstaFetch, than with a state-of-the-art solution.

Web data items such as shopping products, classifieds, and job listings are indispensable components of most e-commerce websites. The information on the data items are typically distributed over two or more webpages, e.g., a ‘Query-Results’ page showing the summaries of the items, and ‘Details’ pages containing full information about the items. While this organization of data mitigates information overload and visual cluttering for sighted users, it however increases the interaction overhead and effort for blind users, as back-and-forth navigation between webpages using screen reader assistive technology is tedious and cumbersome. Existing usability-enhancing solutions are unable to provide adequate support in this regard as they predominantly focus on enabling efficient content access within a single webpage, and as such are not tailored for content distributed across multiple webpages. As an initial step towards addressing this issue, we developed AutoDesc, a browser extension that leverages a custom extraction model to automatically detect and pull out additional item descriptions from the ‘details’ pages, and then proactively inject the extracted information into the ‘Query-Results’ page, thereby reducing the amount of back-and-forth screen reader navigation between the two webpages. In a study with 16 blind users, we observed that within the same time duration, the participants were able to peruse significantly more data items on average with AutoDesc, compared to that with their preferred screen readers as well as with a state-of-the-art solution.

Online discussion forums have become an integral component of news, entertainment, information, and video-streaming websites, where people all over the world actively engage in discussions on a wide range of topics including politics, sports, music, business, health, and world affairs. Yet, little is known about their usability for blind users, who aurally interact with the forum conversations using screen reader assistive technology. In an interview study, blind users stated that they often had an arduous and frustrating interaction experience while consuming conversation threads, mainly due to the highly redundant content and the absence of customization options to selectively view portions of the conversations. As an initial step towards addressing these usability concerns, we designed PView - a browser extension that enables blind users to customize the content of forum threads in real time as they interact with these threads. Specifically, PView allows the blind users to explicitly hide any post that is irrelevant to them, and then PView automatically detects and filters out all subsequent posts that are substantially similar to the hidden post in real time, before the users navigate to those portions of the thread. In a user study with blind participants, we observed that compared to the status quo, PView significantly improved the usability, workload, and satisfaction of the participants while interacting with the forums.

Web data records are usually accompanied by auxiliary webpage segments, such as filters, sort options, search form, and multi-page links, to enhance interaction efficiency and convenience for end users. However, blind and visually impaired (BVI) persons are presently unable to fully exploit the auxiliary segments like their sighted peers, since these segments are scattered all across the screen, and as such assistive technologies used by BVI users, i.e., screen reader and screen magnifier, are not geared for efficient interaction with such scattered content. Specifically, for blind screen reader users, content navigation is predominantly one-dimensional despite the support for skipping content, and therefore navigating to-and-fro between different parts of the webpage is tedious and frustrating. Similarly, low vision screen magnifier users have to continuously pan back-and-forth between different portions of a webpage, given that only a portion of the screen is viewable at any instant due to content enlargement. The extant techniques to overcome inefficient web interaction for BVI users have mostly focused on general web-browsing activities, and as such they provide little to no support for data record-specific interaction activities such as filtering and sorting – activities that are equally important for facilitating quick and easy access to desired data records. To fill this void, we present InSupport, a browser extension that: (i) employs custom machine learning-based algorithms to automatically extract auxiliary segments on any webpage containing data records; and (ii) provides an instantly accessible proxy one-stop interface for easily navigating the extracted auxiliary segments using either basic keyboard shortcuts or mouse actions. Evaluation studies with 14 blind participants and 16 low vision participants showed significant improvement in web usability with InSupport, driven by increased reduction in interaction time and user effort, compared to the state-of-the-art solutions.

Most people who are blind interact with social media content with the assistance of a screen reader, a software that converts text to speech. However, the language used in social media is well-known to contain several informal out-of-vocabulary words (e.g., abbreviations, wordplays, slang), many of which do not have corresponding standard pronunciations. The narration behavior of screen readers for such out-of-vocabulary words and the corresponding impact on the social media experience of blind screen reader users are still uncharted research territories. Therefore we seek to plug this knowledge gap by examining how current popular screen readers narrate different types of out-of-vocabulary words found on Twitter, and also, how the presence of such words in tweets influences the interaction behavior and comprehension of blind screen reader users. Our investigation showed that screen readers rarely autocorrect out-of-vocabulary words, and moreover they do not always exhibit ideal behavior for certain prolific types of out-of-vocabulary words such as acronyms and initialisms. We also observed that blind users often rely on tedious and taxing workarounds to comprehend actual meanings of out-of-vocabulary words. Informed by the observations, we finally discuss methods that can potentially reduce this interaction burden for blind users on social media.

Interaction with web data records typically involves accessing auxiliary webpage segments such as filters, sort options, search form, and multi-page links. As these segments are usually scattered all across the screen, it is arduous and tedious for blind users who rely on screen readers to access the segments, given that content navigation with screen readers is predominantly one-dimensional, despite the available support for skipping content via either special keyboard shortcuts or selective navigation. The extant techniques to overcome inefficient web screen reader interaction have mostly focused on general web content navigation, and as such they provide little to no support for data record-specific interaction activities such as filtering and sorting – activities that are equally important for enabling quick and easy access to the desired data records. To fill this void, we present InSupport, a browser extension that: (i) employs custom-built machine learning models to automatically extract auxiliary segments on any webpage containing data records, and (ii) provides an instantly accessible proxy one-stop interface for easily navigating the extracted segments using basic screen reader shortcuts. An evaluation study with 14 blind participants showed significant improvement in usability with InSupport, driven by increased reduction in interaction time and the number of key presses, compared to state-of-the-art solutions.

Many people with low vision rely on screen-magnifier assistive technology to interact with productivity applications such as word processors, spreadsheets, and presentation software. Despite the importance of these applications, little is known about their usability with respect to low-vision screen-magnifier users. To fill this knowledge gap, we conducted a usability study with 10 low-vision participants having different eye conditions. In this study, we observed that most usability issues were predominantly due to high spatial separation between main edit area and command ribbons on the screen, as well as the wide span grid-layout of command ribbons; these two GUI aspects did not gel with the screen-magnifier interface due to lack of instantaneous WYSIWYG (What You See Is What You Get) feedback after applying commands, given that the participants could only view a portion of the screen at any time. Informed by the study findings, we developed MagPro, an augmentation to productivity applications, which significantly improves usability by not only bringing application commands as close as possible to the user's current viewport focus, but also enabling easy and straightforward exploration of these commands using simple mouse actions. A user study with nine participants revealed that MagPro significantly reduced the time and workload to do routine command-access tasks, compared to using the state-of-the-art screen magnifier.

People with low vision use screen magnifiers to interact with computers. They usually need to zoom and pan with the screen magnifier using predefined keyboard and mouse actions. When using office productivity applications (e.g., word processors and spreadsheet applications), the spatially distributed arrangement of UI elements makes interaction a challenging proposition for low vision users, as they can only view a fragment of the screen at any moment. They expend significant chunks of time panning back-and-forth between application ribbons containing various commands (e.g., formatting, design, review, references, etc.) and the main edit area containing user content. In this demo, we will demonstrate MagPro, an interface augmentation to office productivity tools, that not only reduces the interaction effort of low-vision screen-magnifier users by bringing the application commands as close as possible to the users' current focus in the edit area, but also lets them easily explore these commands using simple mouse actions. Moreover, MagPro automatically synchronizes the magnifier viewport with the keyboard cursor, so that users can always see what they are typing, without having to manually adjust the magnifier focus every time the keyboard cursor goes of screen during text entry.

People with visual impairments typically rely on screen-magnifier assistive technology to interact with webpages. As screen-magnifier users can only view a portion of the webpage content in an enlarged form at any given time, they have to endure an inconvenient and arduous process of repeatedly moving the magnifier focus back-and-forth over different portions of the webpage in order to make comparisons between data records, e.g., comparing the available fights in a travel website based on their prices, durations, etc. To address this issue, we designed and developed TableView, a browser extension that leverages a state-of-the art information extraction method to automatically identify and extract data records and their attributes in a webpage, and subsequently presents them to a user in a compactly arranged tabular format that needs significantly less screen space compared to that currently occupied by these items in the page. This way, TableView is able to pack more items within the magnifier focus, thereby reducing the overall content area for panning, and hence making it easy for screen-magnifier users to compare different items before making their selections. A user study with 16 low vision participants showed that with TableView, the time spent on panning the data records in webpages was significantly reduced by 72.9% (avg.) compared to that with just a screen magnifier, and 66.5% compared to that with a screen magnifier using a space compaction method.

Visual `point-and-click' interaction artifacts such as mouse and touchpad are tangible input modalities, which are essential for sighted users to conveniently interact with computer applications. In contrast, blind users are unable to leverage these visual input modalities and are thus limited while interacting with computers using a sequentially narrating screen-reader assistive technology that is coupled to keyboards. As a consequence, blind users generally require significantly more time and effort to do even simple application tasks (e.g., applying a style to text in a word processor) using only keyboard, compared to their sighted peers who can effortlessly accomplish the same tasks using a point-and-click mouse. This paper explores the idea of repurposing visual input modalities for non-visual interaction so that blind users too can draw the benefits of simple and efficient access from these modalities. Specifically, with word processing applications as the representative case study, we designed and developed NVMouse as a concrete manifestation of this repurposing idea, in which the spatially distributed word-processor controls are mapped to a virtual hierarchical `Feature Menu' that is easily traversable non-visually using simple scroll and click input actions. Furthermore, NVMouse enhances the efficiency of accessing frequently-used application commands by leveraging a data-driven prediction model that can determine what commands the user will most likely access next, given the current `local' screen-reader context in the document. A user study with 14 blind participants comparing keyboard-based screen readers with NVMouse, showed that the latter significantly reduced both the task-completion times and user effort (i.e., number of user actions) for different word-processing activities.

Interacting with long web documents such as wiktionaries, manuals, tutorials, blogs, novels, etc., is easy for sighted users, as they can leverage convenient pointing devices such as a mouse/touchpad to quickly access the desired content either via scrolling with visual scanning or clicking hyperlinks in the available Table of Contents (TOC). Blind users on the other hand are unable to use these pointing devices, and therefore can only rely on keyboard-based screen reader assistive technology that lets them serially navigate and listen to the page content using keyboard shortcuts. As a consequence, interacting with long web documents with just screen readers, is often an arduous and tedious experience for the blind users.To bridge the usability divide between how sighted and blind users interact with web documents, in this paper, we present iTOC, a browser extension that automatically identifies and extracts TOC hyperlinks from the web documents, and then facilitates on-demand instant screen-reader access to the TOC from anywhere in the website. This way, blind users need not manually search for the desired content by moving the screen-reader focus sequentially all over the webpage; instead they can simply access the TOC from anywhere using iTOC, and then select the desired hyperlink which will automatically move the focus to the corresponding content in the document. A user study with 15 blind participants showed that with iTOC, both the access time and user effort (number of user input actions) were significantly lowered by as much as 42.73% and 57.9%, respectively, compared to that with another state-of-the-art solution for improving web usability.

Defect detection is to find unexpected peak regions in an inspection image. Stable Principal Component Pursuit (SPCP) decomposes a given image into three matrices, low-rank, sparsity, and noise which are used for detecting defects. Each of them contains repeated pattern, spatially narrow abnormal elements which are regarded as defects, and small magnitude elements respectively. However, if the noise level of the image is spatially varied, it is hard to separate noise appropriately using naive SPCP. To overcome the difficulty, we propose a novel sliding-window based SPCP algorithm. First, a repeated pattern of each sliding-window is converted to a matrix for SPCP. The noise level based on rank-one approximation is then estimated, and the matrix decomposition is performed. Finally, the sparsity values of all sliding-windows are merged by averaging, and then the averaged term is used for defect detection. The experimental results show that our algorithm outperforms the traditional approaches.