Portfolio of Md Touhidul Islam

Problem:
Blind screen reader users rely entirely on keyboard navigation to interact with desktop applications. However, navigating through menus, toolbars, and dialogs is often tedious and inconsistent across applications. Traditional accessibility evaluation methods either rely on developer compliance checks or require time-consuming user studies, and none provide a scalable way to estimate 'perceived accessibility'—how accessible a software feels to blind users—based on real-life usage. This gap makes comparing or improving applications from a non-visual interaction perspective difficult. 

Solution:

We developed a probabilistic framework to estimate the perceived accessibility of desktop software using keystroke-based screen reader navigation. The framework was inspired by the findings of a formative study with 11 screen reader users. The formative study findings are available in the figures on the left.

Our work in this project:

• Builds a probabilistic interaction model from UI hierarchies extracted via accessibility APIs, without needing pixel data or screen recordings.

• Defines three task-agnostic, user-aligned metrics:

  • Complexity – expected keystrokes to reach a target (see Fig. 10).

  • Coverage – percentage of interface reachable within k keystrokes.

  • Reachability – fraction of the interface that can be accessed efficiently with shortcuts.

• Validates these metrics through another study with the same 11 participants as the formative study.

• Benchmarks 11 widely used Windows applications, including Notepad, Spotify, and Microsoft Word (see Fig. 10).