Handwritten Digit Recognition by Elastic Matching

TL;DR

This paper introduces a simple, invariant handwritten digit recognition model based on elastic matching, inspired by face recognition theory, emphasizing interpretability and simplicity over high accuracy.

Contribution

It adapts face recognition theory to handwritten digit recognition, achieving invariance without extensive training, and offers a transparent, simple model for further development.

Findings

Recognition rates are lower than other methods.

Model achieves translation and rotation invariance.

System is simple, interpretable, and suitable for future improvements.

Abstract

A simple model of MNIST handwritten digit recognition is presented here. The model is an adaptation of a previous theory of face recognition. It realizes translation and rotation invariance in a principled way instead of being based on extensive learning from large masses of sample data. The presented recognition rates fall short of other publications, but due to its inspectability and conceptual and numerical simplicity, our system commends itself as a basis for further development.