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Pruning Network Based Knowledge Distillation for Offline Handwritten Chinese Character Recognition

Recently, deep convolutional neural networks have brought great breakthrough in image classiﬁcation, which provide eﬀective solution for the handwritten Chinese character recognition problem. Researchers have experimented with various networks to increase recognition accuracy. Although good accuracy is achieved on diﬀerent networks, these networks tend to be computation-intensive and memory-intensive that make them diﬃcult to be deployed on resource-constrained devices. To solve the problem, the paper proposes an optimization to reduce the number of model parameters by using pruning network and knowledge distillation. Besides, to improve the model’s ability to extract the input features, an attention mechanism is adopted in the proposal. The experimental results show that the number of parameters decreased by nearly 26%. At the same time, the recognition accuracy improves by 1.17% with the value of 96.99% compared with the original model. The optimization method presented in this paper not only improves the accuracy of handwritten Chinese characters recognition but also reduces the number of model parameters.