Deep Learning and SVM-Based Approach for Indian Licence Plate Character Recognition

Every developing country relies on transportation, and there has been an exponential expansion in the development of various sorts of vehicles with various configurations, which is a major component strengthening the automobile sector. India is a developing country with increasing road traffic, which has resulted in challenges such as increased road accidents and traffic oversight issues. In the lack of a parametric technique for accurate vehicle recognition, which is a major worry in terms of reliability, high traffic density also leads to mayhem at checkpoints and toll plazas. A system that combines an intelligent domain approach with more sustainability indices is a better way to handle traffic density and transparency issues. The Automatic Licence Plate Recognition (ALPR) system is one of the components of the intelligent transportation system for traffic monitoring. This study is based on a comprehensive and detailed literature evaluation in the field of ALPR. The major goal of this study is to create an automatic pattern recognition system with various combinations and higher accuracy in order to increase the reliability and accuracy of identifying digits and alphabets on a car plate. The research is founded on the idea that image processing opens up a diverse environment with allied fields when employing distinct soft techniques for recognition. The properties of characters are employed to recognise the Indian licence plate in this study. For licence plate recognition, more than 200 images were analysed with various parameters and soft computing techniques were applied. In comparison to neural networks, a hybrid technique using a Convolution Neural Network (CNN) and a Support Vector Machine (SVM) classifier has a 98.45% efficiency.