Publication Type : Conference Paper
Publisher : Image Processing and Capsule Networks
Source : Image Processing and Capsule Networks, Springer International Publishing, Cham (2021)
Url : https://link.springer.com/chapter/10.1007/978-3-030-51859-2_7
ISBN : 9783030518592
Campus : Coimbatore
School : School of Engineering
Department : Electrical and Electronics
Year : 2021
Abstract : Identification of License plates of vehicles is significant in various monitoring and security applications. This paper involves two major processes: image processing of the License plate and classification of the individual characters. Preprocessing of the number plate image is done for noise removal by converting it to a grayscale image followed by conversion to a binary image. The Bounding Box method is implemented for the segmentation of individual characters that are to be classified. Two Supervised machine learning algorithms namely Support Vector Machine (SVM) and Decision Tree is used for the classification. The performance indices of the two algorithms are analysed to determine the more accurate method. It is proposed to implement the algorithm on a stand-alone model to employ it for real-time applications after improving the accuracy with more training and test data.
Cite this Research Publication : Anjali Suresan, Divyaa Mahalakshm G., Meenakshi Venkatraman, Shruthi Suresh, and Supriya P., “Comparison of Machine Learning Algorithms for Smart License Number Plate Detection System”, in Image Processing and Capsule Networks, Cham, 2021.