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Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which child was...

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A Standardized Obstacle Course for Assessment of Visual Function in Ultra Low Vision and Artificial Vision

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Published on: February 11, 2014

VI-OCR: "Visually Impaired" optical character recognition pipeline for text accessibility assessment.

Qingying Gao^1,2, Roberto Manduchi³, Pradeep Y Ramulu²

¹Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.

Scientific Reports

|December 10, 2025

Summary

We developed VI-OCR, a tool using AI to assess text accessibility for people with low vision. It mimics human reading abilities to ensure text is readable, improving product design for visually impaired individuals.

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Area of Science:

Computer Vision
Ophthalmology
Human-Computer Interaction

Background:

Low vision significantly impairs daily activities, especially reading.
Current product design often neglects the needs of low vision readers due to challenges in quantifying text accessibility.

Purpose of the Study:

To introduce VI-OCR, a novel text accessibility assessment pipeline.
To bridge the gap between computer vision and low vision research.
To estimate text recognizability for low vision readers based on visual acuity and contrast sensitivity.

Main Methods:

Developed VI-OCR using state-of-the-art Optical Character Recognition (OCR) models.
Benchmarked OCR and vision-language models on letter acuity (ETDRS charts), word acuity (MNREAD charts), and scene text recognition.
Compared model performance to normal vision participants under simulated visual deficits.

Main Results:

Identified limitations in some models, including poor generalizability and difficulty with contrast reduction.
Observed that some models overperformed human capabilities rather than mimicking them.
Demonstrated that advanced models like Qwen2.5-VL and GPT show human-like performance.

Conclusions:

VI-OCR is a feasible approach for assessing text accessibility for the visually impaired.
Human-like performance in AI models supports the utility of VI-OCR for inclusive design.
Further research can refine models to better mimic human visual perception for low vision readers.