An character recognition network for imprint character
The imprint characters on the surface of the workpiece are uneven,rusty,and weathered,which the traditional character recognition methods hard to achieve satisfactory results.This paper regards the characters recognition task as a particular detection problem and designs a two-stage recognition network according to its characteristics:location and classification network.The location newtork uses the anchor-free method to extract the region of interest of characters,which effectively solves the problem of character region extraction.The classification network uses the Feature Decoupled Convolution Block and the Structural Re-parameterization technology,which can significantly improve the classification accuracy without any extra parameter.The transferring learning is used to solve the small sample problem and imbalance problem in the training stage.The experimental results on the self-built bolt dataset and the SynthText dataset show that the algorithm can achieve overall accuracies of 98%and 92%,respectively,which is superior to the compared algorithms.
万方数据
维普
