A Study of Designing Compact Classifiers Using Deep Neural Networks for Online Handwritten Chinese Character Recognition
Jun Juan Du, Jinshui Hu, Bo Zhu, Si Wei, Li-Rong Dai · 2014
This paper presents a study of designing compact classifiers using deep neural networks for recognition of online handwritten Chinese characters. Two schemes are investigated based on practical considerations. First, deep neural networks are adopted purely as a classifier with a state-of-the-art feature extractor of online handwritten Chinese characters. Second, the so-called bottleneck features extracted from a bottleneck layer of deep neural networks are fed to the prototype-based classifier. The experiments on an in-house developed online Chinese handwriting corpus with a vocabulary of 15,167 characters show that compared with prototype-based classifier widely developed on the mobile device, deep neural network based classifier can yield significant improvements of recognition accuracy with acceptably increased footprint and latency while the bottleneck-feature approach can bring a more compact classifier with an observable performance gain.