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ORIGINAL RESEARCH article

Front. Artif. Intell.

Sec. AI for Human Learning and Behavior Change

Volume 8 - 2025 | doi: 10.3389/frai.2025.1676644

Enhancing Ishihara and Educational Images Using Machine Learning: Toward Accessible Learning for Colorblind Individuals

Provisionally accepted
Aahan  Ritesh PrajapatiAahan Ritesh Prajapati1Ajay  GoyalAjay Goyal2*
  • 1Adani International School, Adani Shantigram, Ahmedabad, Gujarat, India
  • 2Nirma University Institute of Design, Ahmedabad, India

The final, formatted version of the article will be published soon.

Color Vision Deficiency (CVD) affects over 300 million individuals worldwide, with protanopia and deuteranopia being the most common subtypes, causing red–green confusion. This study leverages machine learning to (a) classify reference (considered as normal vision) and simulated protanopia and deuteranopia Ishihara plate images, (b) generate corresponding enhanced versions of these images, and (c) provide improved textbook diagrams (from NCERT books) and other pseudochromatic figures for CVD students, validated through feedback from diagnosed individuals. Tritanopia and milder forms of CVD were excluded in this study. A dataset of 1400 Ishihara plates was processed to simulate protanopia and deuteranopia perception via standard Red Green Blue (sRGB) to long-, medium-, and short-wavelength cone (LMS) modeling. Enhanced images were generated using a daltonization function defined by the error between reference and simulated images, with enhancement strength (α) optimized to maximize contrast gain while minimizing distortion. Feature embeddings from ResNet-50, EfficientNet-B0, and DenseNet-201 were fused and reduced via PCA, followed by One-vs-All (OvA) (classifiers: linear support vector machine, logistic regression, and decision tree), random forest, gradient boosting, and neural network. Results showed optimal enhancement at α = 0.54 for deuteranopia and 0.64 for protanopia, achieving contrast gains of 69.6 and 64.3, respectively, with minimal color distortion (ΔE ≈ 4.9) and negligible clipping (<0.002). The OvA strategy achieved 99.7% accuracy, while MLP reached 100% across metrics. Surveys with 15 diagnosed students confirmed substantial perceptual improvement: recognition of previously unreadable digits and symbols increased from <20% to full visibility, with mean ratings above 4/5 for enhanced images. The OvA technique integrated with daltonization can assist in enhancing Ishihara and educational images in real time.

Keywords: color vision deficiency (CVD), protanopia, Deuteranopia, Ishihara plates, Daltonization, machine learning, LMS cone space, Educational image accessibility

Received: 30 Jul 2025; Accepted: 29 Sep 2025.

Copyright: © 2025 Prajapati and Goyal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Ajay Goyal, ajay.goyal@nirmauni.ac.in

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