# An MLP Baseline for Handwriting Recognition Using Planar Curvature and Gradient Orientation

arXiv: https://arxiv.org/abs/2508.11803 · HTML: https://mlsanity.com/paper/2508.11803/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 15 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [EMNIST: an extension of MNIST to handwritten letters](https://mlsanity.com/paper/1702.05373/) (2017)

## Built on by (most cited first, 4 of 4)

- [A Sobel-Gradient MLP Baseline for Handwritten Character Recognition](https://mlsanity.com/paper/2508.11902/) (2025)
- [Significance-Gain Pair Encoding for LLMs: A Statistical Alternative to Frequency-Based Subword Merging](https://mlsanity.com/paper/2603.19261/) (2026)
- [StepCache: Step-Level Reuse with Lightweight Verification and Selective Patching for LLM Serving](https://mlsanity.com/paper/2603.28795/) (2026)
- [MDL-Calibrated Significance-Gain Pair Encoding: Replication-Aware Automatic Stopping for Subword Tokenization](https://mlsanity.com/paper/2609.31705/) (2026)

## Abstract (arXiv)

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 alternative to convolutional neural networks (CNNs). Using these three handcrafted feature maps as inputs, our curvature-orientation MLP achieves 97 percent accuracy on MNIST digits and 89 percent on EMNIST letters. These results underscore the discriminative power of curvature-based representations for handwritten character images and demonstrate that the advantages of deep learning can be realized even with interpretable, hand-engineered features.