  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.
