Recognizing car license plates plays a vital role in control and surveillance systems. However, manually identifying number plates for parked or passing vehicles is a demanding and time-consuming task. In this paper, we introduce a training-oriented method for vehicle license plate recognition. In contrast to earlier automatic license plate recognition (ALPR) systems, which are constrained by factors like fixed backgrounds, indoor settings, limited vehicle speeds, specific driveways, consistent lighting, or predefined camera-tovehicle distances, our objective is to develop a resilient recognition model that excels across various lighting conditions and angles [1]. We trained our model using a carefully curated car license plate dataset and the TensorFlow Object Detection API. To assess its performance, we conducted extensive testing on a dataset comprising 24,000 images with diverse colors and lighting conditions. The results demonstrate the efficacy of our approach in achieving precise and robust license plate recognition even in challenging real-world scenarios.