The identification and analyzing of license number plates is a critical component of many traffic surveillance applications, including traffic law enforcement, vehicle tracking, and security systems. To increase the precision and speed of license number plate identification in traffic surveillance scenarios, the study suggests an effective license number plate detection and recognition system. There are two basic stages of the current system are registration plate identification and encoding of number. In the identification stage, recurrent neural network (RNN) architecture is employed to identify potential license plate regions from the input photos. To learn distinguishing features and achieve high accuracy in plate localization, the RNN model is trained using a big dataset of annotated license plate photos. After that, unnecessary bounding boxes are eliminated for the purpose to improve efficiency, using a marginal suppression strategy. In the recognition stage, characters are extracted from the observed license plate regions using a deep learning-based optical character recognition (OCR) model. To precisely identify the alphanumeric symbols on license plates, the OCR model is trained on a diverse collection of labeled characters. In conclusion, this research presents an efficient license number plate’s identification and analyzing system using deep neural networks. The suggested approach provides an effective solution for traffic surveillance applications, providing accurate license plate identification while ensuring real-time performance.