Automated License Plate Recognition plays an important role in current traffic control and monitoring systems. This work discusses a combined solution for automated License Plate Recognition (LPR) by integrating the EasyOCR library with the YOLOv8 object detection model. The proposed solution makes use of deep learning and computer vision applications. This system includes the text-to-speech functionality for reading out the information from recognized license plate. Also the system is linked with the transport-related database for retrieving vehicle information of the recognized license plate. This highlights how the system is useful in real-world applications. The work consists of an extensive pipeline that produces a dependable and effective LPR system, starting with object identification based on YOLOv8 and ending with character recognition based on EasyOCR. The suggested system has a precision of 98.7%, recall of 98.8%, and an \(F_1\) -score of 98.7% for recognizing license plates. The system’s open source nature enables for further enhancements and modification, encouraging collaboration and innovation in computer vision and license plate recognition.