An Agile and Efficient Neural Network Based on Knowledge Distillation for Scene Text Detection

Text detection is increasingly in demand recently and poses significant challenges for the tradeoff among the detection accuracy, memory resources, and inference speed in the case of applying to the portable device such as mobile phones. Current methods mainly focus on the detection accuracy but neglect either the running speed or the memory consumption. To this end, an agile and efficient neural network for scene text detection that balances the detection performance, running speed, and the model size is hereby proposed. In order to reduce the network parameters and speed up, the neural network for text detection is firstly pruned; and then, the pruned neural network is trained with the structured knowledge distillation for improving the detection performance. The method is implemented on three benchmark text datasets, i.e., ICDAR2015, Total-Text, and MSRA-TD500. The experimental results demonstrate that the hereby proposed method achieves the best comprehensive performance with a faster running speed and much less memory resources while the text detection accuracy is comparable to that acquired using the excellent text detection methods.