Computers, Volume 14, Issue 2
February 2025 - 46 articles
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Cover Story: Optical character recognition (OCR) has made remarkable progress with deep learning, yet challenges persist in scenarios involving color distortions, such as those experienced by individuals with color blindness. This paper investigates the robustness of convolutional neural networks (CNNs) against red–green distortions by leveraging the Ishihara-Like MNIST dataset. By training and evaluating six distinct architectures, including MNIST, LeNet5, VGG16, AlexNet, and two custom models, this work explores architectural changes and hyperparameter tuning to enhance model performance. Our findings demonstrate the significant improvements over prior benchmarks, offering insights into CNN adaptability in visually challenging environments. View this paper