Innovative Image Preprocessing Methods for Enhancing OCR Accuracy in Number Plate Detection

The accuracy of Optical Character Recognition (OCR) systems in number plate detection is critically dependent on the quality of image preprocessing techniques. This research introduces a novel hybrid preprocessing approach that integrates adaptive thresholding with edge detection and morphological operations to enhance the performance of OCR systems in recognizing vehicle number plates. The study evaluates various combinations of preprocessing techniques, including the integration of thresholding method with Canny and Sobel filter, to optimize the OCR pipeline. Experimental results demonstrate a significant increase in OCR accuracy, particularly under challenging conditions such as low light and high noise levels. The proposed hybrid approach outperforms traditional methods, offering a robust solution for improving the reliability and efficiency of number plate recognition systems in real-world applications.