On-board screen text images are rich in content,diverse in background s,large in text information,and indeterminate in size and direction,resulting in low accuracy and slow detection speed in the process of text detection and recognition.Therefore,a system for detecting and recognizing text on the vehicle′s screen based deep learning-based approach in this paper is proposed.The text detection model of the system applies an improved EAST networ k,and uses the DenseNet network as a feature extraction network to enhance feature reuse.I n order to improve the prediction of long texts,a text line construction method for predic ting vertex positions of regional boundary elements is used,and an improved text detection model of DenseEAST network is put forward.The CRNN-X text recognition model of the reco gnition framework is proposed,the deep separable convolution based on the CRNN model is introduced to reduce the amount of network parameters,and the network adds dropout to opti mize the training process.The experimental results show that the improved method for tex t detection and text recognition in the open data set and actual on-board screen scenes d has h igh accuracy.The accuracy of text detection and text recognition of the model in the actual vehic le screen scene is 97.3% and 97.6% respectively,which can meet the use requirements in the actual scene.