Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition

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.

The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review

“…In 2021, Dong et al [74] proposed an interpretable distance metric learning method. First, an algorithm named MetChar was proposed to optimize the weight distribution of fixed components.…”

Handwritten Recognition Techniques: A Comprehensive Review

“…This research addressed a niche within the formal digitization devices for Devanagari and progressed programmed content recognition innovation for superior client benefit. Dong et al [91] introduced a comprehensible method for distance metric learning. Initially, they developed an algorithm called MetChar to optimize the weight distribution among fixed components.…”