Template-Instance Loss for Offline Handwritten Chinese Character Recognition

TL;DR

This paper introduces template and instance loss functions for offline handwritten Chinese character recognition, addressing character diversity and cursive writing challenges, leading to state-of-the-art results.

Contribution

It proposes novel loss functions tailored for Chinese character recognition, improving accuracy over previous methods.

Findings

Achieves state-of-the-art recognition accuracy.

Effectively handles character similarity and cursive handwriting.

Outperforms existing methods on benchmark datasets.

Abstract

The long-standing challenges for offline handwritten Chinese character recognition (HCCR) are twofold: Chinese characters can be very diverse and complicated while similarly looking, and cursive handwriting (due to increased writing speed and infrequent pen lifting) makes strokes and even characters connected together in a flowing manner. In this paper, we propose the template and instance loss functions for the relevant machine learning tasks in offline handwritten Chinese character recognition. First, the character template is designed to deal with the intrinsic similarities among Chinese characters. Second, the instance loss can reduce category variance according to classification difficulty, giving a large penalty to the outlier instance of handwritten Chinese character. Trained with the new loss functions using our deep network architecture HCCR14Layer model consisting of simple layers, our extensive experiments show that it yields state-of-the-art performance and beyond for offline HCCR.