Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition

TL;DR

This paper introduces an interpretable distance metric learning method for handwritten Chinese character recognition, improving accuracy and interpretability over existing approaches by combining base metrics.

Contribution

The paper presents a novel linear combination-based interpretable distance metric learning approach tailored for handwritten Chinese character recognition.

Findings

Superior accuracy on benchmark datasets

Enhanced interpretability of the learned metrics

Efficient recognition performance

Abstract

Handwriting recognition is of crucial importance to both Human Computer Interaction (HCI) and paperwork digitization. In the general field of Optical Character Recognition (OCR), handwritten Chinese character recognition faces tremendous challenges due to the enormously large character sets and the amazing diversity of writing styles. Learning an appropriate distance metric to measure the difference between data inputs is the foundation of accurate handwritten character recognition. Existing distance metric learning approaches either produce unacceptable error rates, or provide little interpretability in the results. In this paper, we propose an interpretable distance metric learning approach for handwritten Chinese character recognition. The learned metric is a linear combination of intelligible base metrics, and thus provides meaningful insights to ordinary users. Our experimental results on a benchmark dataset demonstrate the superior efficiency, accuracy and interpretability of our proposed approach.