Automatic recognition of Indian vehicles license plates using machine learning approaches. (2022)

• Record Type: Journal Article
• Authors: Kumar Sahoo, Ashok
• Is Part Of: Materials today. Volume 49:Part 8(2022)
• Page Start: 2982
• Page End: 2988
• Publication Date: 2022
• Subjects: Indian vehicle number plate -- k-nearest neighbor -- Decision tree -- Neural network -- Local histogram -- Hierarchical centroid; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.09.046
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: A proposed method for automation of Indian vehicles number plate conversion is presented in this paper. Many vehicles are plying on Indian roads with varying in size and structure and hence there are a number of variations in number plates too. Although there are strict norms to be followed for number plate designs, however, it can be seen many deviations in actual number plates of many vehicles. It can be also observed that the number plates are written in many Indian languages, but the prescribed norm is Hindu-Arabic numerals with Latin letters which is prescribed by the licensing plate issuing authority. The number plate designs are varied according to types of vehicles, like, personal, commercial, public transport, etc. In order to design an automatic vehicle number plate, considerations of these factors are taken into account. The steps in the design include segmentation and character extraction from number plates and recognition of segmented characters in order to extract the complete information from number plates. The work in this paper is focused on Hindu-Arabic numerals with Latin letters. For feature extraction, Prewitt filter technique is used to extraction of characters from the vehicle number plates and connected component analysis is used for segmenting characters. Three different classifiers, namely, k-Nearest Neighbor, Artificial Neural Network and Decision Tree, are used in the experiments. Recognition accuracy up to 98.10% is achieved and is a promising factor for future research in this direction.