Bringing Things Closer: Enhancing Low-Vision Interaction Experience with Office Productivity Applications

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.

Mobile Text Reader for People with Low Vision

People with low vision have visual acuity less than 6/18 and at least 3/60 in the better eye, with correction. The limited vision requires them to enhance their reading ability using magnifying glass or electronic screen magnifier. However, people with severe low vision have difficulty and suffer fatigue from using such assistive tool. This paper presents the development of a mobile text reader dedicated for people with low vision. The mobile text reader is developed as a mobile application that allows user to capture an image of texts and then translate the texts into audio format. One main contribution of this work compared to typical optical character recognition (OCR) engines or text-to-speech engines is the addition of image stitching feature. The image stitching feature can produce one single image from multiple poorly aligned images, and is integrated into the process of image acquisition. Either single or composite image is subsequently uploaded to a cloud-based OCR engine for robust character recognition. Eventually, a text-to-speech (TTS) synthesizer reproduces the word recognized in a natural-sounding speech. The whole series of computation is implemented as a mobile application to be run from a smartphone, allowing the visual impaired to access text information independently.