Projects / FormA11y

Making dense PDF forms accessible without expert-only tools
PDF forms are rarely born accessible. Before someone using a screen reader can complete one, an author has to identify every field, assign the right field type and bounds, group related controls, and write useful labels and tooltips. Existing tools make that work non-intuitive, repetitive, and overwhelming, especially when a form contains hundreds of tightly packed elements.

FormA11y reframed remediation as a guided human-in-the-loop workflow. Machine-learning models produced a partially remediated starting point; the person reviewed fields, groups, and tooltips one layer at a time and corrected what the model missed. The aim was not to remove human judgment, but to spend it where it mattered.

Research
We interviewed two PDF-remediation specialists with more than seven and ten years of experience, reviewed WCAG guidance, and audited accessible government forms. The same three problems kept appearing: the workflow demanded specialized knowledge, repeated the same actions across every field, and forced people to hold too much visual information in their attention at once.

We then ran 20 pilot tests across low-fidelity Figma concepts and a working React prototype. At least two people tested each minor iteration, and four people completed an end-to-end pilot of the final study protocol. Those sessions moved the design from broad flows to granular interactions such as drawing a field, changing its type, grouping controls, and linking OCR text to a tooltip.

Iterations
There were many more concepts than the final paper could foreground. These are two of the larger directions we explored while trying to balance guidance, cognitive load, and the amount of repetitive review a person still had to do.

Iteration 1 · Auto-move
The flow began with active learning: FormA11y surfaced the fields in which the model had low confidence and asked the person to correct them first. It then moved through every field automatically. The person could pause or flag problems with bounds, type, grouping, or labels, remediate those exceptions, and use the corrections to improve suggestions elsewhere in the form.

Active-learning step isolating one low-confidence State field in a dense PDF form

Start with uncertainty
Dimming the rest of the page focused attention on one model output at a time, with a simple count showing how much uncertain work remained.

Auto-move review step highlighting one Social Security Number field with flags across the form

Review without losing your place
Auto-move advanced through the form field by field. People could stop the sequence, move backward or forward, and flag an issue without having to remember which fields they had already checked.

Remediation overview showing flagged fields distributed across a long PDF form

Collect exceptions, then fix them
The remediation step gathered flagged items into a focused queue, so review and correction were separate modes instead of one constantly shifting task.

Focused remediation view for changing a field type, label, or bounds
Field-type menu with text, checkbox, radio, date, and signature options

Correct in context
A floating toolbar kept common fixes next to the selected field: resize its bounds, change its type, update the label, or delete it. The concept had strong wayfinding, but moving through every field could still feel long on a dense form.

Iteration 2 · Sections
The next direction reduced scope. A person selected one horizontal section and reviewed its fields, groups, labels, and tooltips before moving on. The hypothesis was that a smaller working area would make dense forms less overwhelming, while corrections from each section would improve model suggestions and leave fewer errors later.

A movable orange separator defining the active section of a PDF form

Constrain the working area
Only the selected slice stayed interactive; content above and below was muted to prevent accidental changes and reduce the visual field.

Fields layer showing color-coded text, radio, and checkbox fields in one selected section
Groups layer showing related radio and checkbox controls outlined together
Tooltips layer showing editable labels attached to fields in the selected section

The learning
The layered review was valuable, but the section metaphor was not. In pilots with three people, participants struggled to form a consistent mental model, often drew page-height sections, and felt that repeatedly defining a region and pressing “next” added work. We removed sections and kept the stronger idea: show only the layer relevant to the task at hand.

User study and results
The final evaluation was a counterbalanced within-subject study with 20 participants: 4 experienced PDF-form remediators and 16 novices. Everyone used both FormA11y and Adobe Acrobat Pro on two government forms matched for difficulty and error count; half started with each tool to control for learning effects. We measured completion time, remaining accessibility errors, task difficulty, confidence, and System Usability Scale scores.

2.8×
faster than Acrobat
82.4%
fewer remediation errors
83.4
SUS score, vs 45.6

Participants completed the work in 41% of the time required in Acrobat: a mean of 12:55 versus 31:29. All 20 finished within the time limit in FormA11y, compared with 70% in Acrobat. They also left significantly fewer errors across fields, groups, and tooltips. The time difference was significant (two-tailed unequal-variance t-test, p = 6.54 × 10⁻⁹); Wilcoxon signed-rank tests were also significant for fields and tooltips (p = 0.0002) and groups (p = 0.0003).

Read the full ACM research paper.

Final prototype walkthrough
The video documents the complete research and final workflow. This prototype was a research instrument built to survive real user studies, not a visual-design endpoint. While designing the system, I also wrote the working prototype by hand, ran the studies, analyzed the data, and led the paper. I prioritized reliable interactions and research validity over the final layer of visual polish.

Sparsh Paliwal · 2025