Abstract
Most existing systems use portable devices or image processing techniques for handwritten Chinese character recognition (HCCR), which are unable to detect character when writing on a paper or sensitive to lighting conditions. In this article, we present the design, implementation, and evaluation of a smartwatch-based HCCR system, called SmartHandwriting. To segment each Chinese character, we further analyze the hand movement between the handwriting gesture and the wrist movement gesture and propose a novel algorithm to distinguish the two types of gestures. Due to too many Chinese characters for classification, we utilize the data augmentation method for avoiding overfitting. Then, we build the HCCR model using the deep convolutional neural network (DCNN) method. The recognition accuracy of the Chinese characters is 96.0%, and extensive experiments confirm its effectiveness and robustness. Moreover, we also explore adverse factors that affect the recognition performance, which can be avoided in the future.
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