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An MLP Baseline for Handwriting Recognition using Planar Curvature and Gradient Orientation
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 47 |
| Year of Publication: 2025 |
| Authors: Azam Nouri |
10.5120/ijca2025925791
|
Azam Nouri . An MLP Baseline for Handwriting Recognition using Planar Curvature and Gradient Orientation. International Journal of Computer Applications. 187, 47 ( Oct 2025), 1-5. DOI=10.5120/ijca2025925791
Abstract
This study investigates whether second-order geometric cues—planar curvature magnitude, curvature sign, and gradient orientation—are sufficient on their own to drive a multilayer perceptron (MLP) classifier for handwritten character recognition (HCR), offering an interpretable alternative to convolutional neural networks (CNNs). Using these three handcrafted feature maps as inputs, the curvature–orientation MLP achieves 97%accuracy on MNIST digits and 89%on EMNIST letters. These results underscore the discriminative power of curvature-based representations for handwritten character images and demonstrate that competitive performance is achievable with lightweight, explicitly engineered features.
References
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