Number Plate Detection, employing image processing, addresses the prevalent issues of traffic rule violations and vehicle thefts. A 2019 survey by The Hindu highlighted the substantial number of stolen vehicles (44,158) and violations (1,85,210) in 2018. Its objective is to develop an efficient model for automatically identifying license plates from captured images, applicable in entrance zones for security control and restricted areas like government offices and military zones. The system involves capturing images, detecting number plate areas, and utilizing Optical Character Recognition (OCR) to extract owner information. Recognizing the global challenge of number plate detection, the study compares Support Vector Machine (SVM) and Convolutional Neural Network (CNN) algorithms, using a Kaggle dataset. OpenCV is employed for image pre-processing, reducing processing time. Results indicate SVM outperforms CNN, achieving 89.02% accuracy compared to CNN's 77.83%, emphasizing its potential in enhancing security measures and addressing the increasing need for effective number plate detection systems worldwide.