<![CDATA[Automatic license plate recognition is a vital component in the development of intelligent transportation systems and security management based on digital imagery. This study examines the implementation of deep learning using algorithm to detect and recognize vehicle license plates from visual images. Using 472 sample images of license plates taken under varying lighting conditions, camera angles, and background complexities, the research involved manual data labeling and trained an end-to-end object detection model. Google Colab was employed to train the model, allowing efficient and cost-free GPU computation. After training, the system was tested for its ability to detect license plate regions, followed by character extraction using Optical Character Recognition (OCR). Experimental results show that the model accurately detects license plate regions with a detection accuracy exceeding 90%, and successfully reads most alphanumeric characters, despite challenges such as image blur and partial occlusion. These findings demonstrate that the a reliable solution for license plate recognition systems powered by artificial intelligence. Furthermore, this research offers potential for integration into automated edge devices and intelligent traffic management systems.]]>
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