Many existing approaches perform poorly in the detection and recogniti on of license plates in complex scenes.So the LPDR-RSCNet-an end-to-end and uni fied network for the detection and recognition of license plates in a single for ward pass was proposed.In this network,the detection and recognition modules a re jointly trained to reduce intermediate error accumulation with residual neura l network,spatial transformer network and connectionist temporal classification .By introducing spatial transformation network into the residual neural network feature extraction process,the extractor has translation invariance,rotation invariance and scaling invariance;by introducing connection time series classif ication into classifier,the relationship between image label and feature can be recognized automatically.At the same time,the network can also be adapted to the recognition of variable length sequences.The experiments are conducted on C CPD which is a large-scale and diverse Chinese license plate dataset.Compared w ith YOLO9000,Fast R-CNN and SSD300,the LPDR-RSCNet model has been proved to be more effective,greatly improves detection accuracy of license plates in comple x field scene performance.In practical applications,this model achieves 98.8% recognition accuracy and 34fps speed.This model can meet the requirements of re al-time license plate detection and recognition of moving vehicles in Intellige nt Traffic System.