Enhancing Image Recognition of Damaged Number Plate in the Running Vehicle using Novel Genetic Algorithm Compared with Morphological Erosion Algorithm

Abstract

Aim: Automatic detection of damaged number plates of the vehicle using image processing algorithms and improving the accuracy rate of readability in recognition of the number plates.  Materials and methods: Two sample groups using 10 images from the sample dataset, which is tested at 80% for G power with t-test analysis. To improve the accuracy of recognition, the novel genetic algorithm is proposed and compared with the morphological erosion algorithm. Results: Test results prove that the novel genetic algorithm has an accuracy of 93.5 %, which seems to be better than the morphological erosion algorithms accuracy of 91.5%. Since the significance is around 0.46, there is a statistically significant difference among the study group with (p < 0.05). Conclusion: For distorted and damaged images, the detection and recognition of number plates using the genetic method seems to appear better than the morphological erosion algorithm. Detection of violations using road side cameras can perform better with our proposed work.