A discriminative classifier for in-air handwritten Chinese characters recognition

Abstract

As the development of three-dimensional interaction, in-air writing arises as a novel interaction method, and brings traditional writing behavior to the 3D space. In this paper, we propose a discriminative three-level classifier for in-air handwritten Chinese character recognition. Since the existence of characters with similar shapes and structures results in one of the main difficulties of Chinese handwritten character recognition, this paper tackles the problem caused by similar characters using two discriminative analysis techniques. First, we exploit the learning vector quantization (LVQ) to obtain discriminative prototypes. Then, we use the adaptive discriminative locality alignment (ADLA) to distinguish candidate character class among similar character classes. In the experiments, we evaluate the proposed method on the IAHCC-UCAS2014 dataset constructed by ourselves using the writing-in-the-air system based on the Leap Motion Controller. The experimental results show that the proposed method obtain higher recognition accuracy with lower computational cost.