Multi-language Text Detection and Recognition Based on Deep Learning

In order to improve the accuracy of rotated text detection effectively, we combine the network architectures with different functions to make up for the shortcomings of each network. First, rotate proposal regions to achieve scene text detection in any direction. We also enhance the features of important channels and weaken the features of non-important channels to effectively improve the accuracy of the overall network. In order to achieve multi-scale target detection in the scene, the chips of the same size are intercepted on different scales. Therefore, it can zoom in on smaller targets, and zoom out on larger targets. The experiments show that this method is competitive in accuracy with traditional architecture.