Noise, Masking, and Translation Robustness of Multilayer Perceptrons for Handwritten Digit Recognition
K Kiruthika
Digital Signal Processing and Artificial Intelligence for Automatic Learning, Vol. 5, No. 3, 2026.
DOI: https://doi.org/10.6025/dspaial/2026/5/3/168-186
Link: https://www.dline.info/dspai/fulltext/v5n3/dspaiv5n3_2.pdf
Abstract:
Handwritten digit recognition systems are often expected to perform reliably under imperfect input conditions,
yet simple neural architectures are frequently evaluated only on clean benchmark data. This study
systematically evaluates the robustness of a multilayer perceptron (MLP) trained on the MNIST handwritten
digit dataset under four controlled perturbation families: additive Gaussian noise, salt and pepper impulse
noise, contiguous pixel masking, and spatial translation. The trained MLP achieves a clean test accuracy of
92.75%. Perturbation analysis using accuracy, precision, recall, F1-score, confusion matrices, and accuracyversus-
strength curves shows that the model is comparatively resilient to sparse and localized degradations,
maintaining 86.40% accuracy under salt and pepper noise and 82.95% under pixel masking. Performance
decreases more noticeably under Gaussian noise, with accuracy falling to 77.35%. In contrast, even small
translations cause severe degradation, with accuracy dropping to 33.35% at a 2-pixel shift and below 5% at
shifts of 4â€“5 pixels. Gradient-based saliency maps reveal that the network relies on localized stroke pixels at
fixed spatial positions, explaining its pronounced vulnerability to translation. The findings demonstrate that
MLP robustness is highly perturbation-specific and fundamentally constrained by the absence of spatial
inductive biases such as weight sharing and local connectivity. The study provides practical guidance for
deploying lightweight classifiers and highlights the importance of preprocessing or translation invariant
architectures in real world handwriting recognition applications.