License Plate Recognition System Using Artificial Neural Networks

Abstract

A high performance license plate recognition system (LPRS) is proposed in this work. The proposed LPRS is composed of the following three main stages: (i) plate region determination, (ii) character segmentation, and (iii) character recognition. During the plate region determination stage, the image is enhanced by image processing algorithms to increase system performance. The rectangular license plate region is obtained using edge‐based image processing methods on the binarized image. With the help of skew correction, the plate region is prepared for the character segmentation stage. Characters are separated from each other using vertical projections on the plate region. Segmented characters are prepared for the character recognition stage by a thinning process. At the character recognition stage, a three‐layer feedforward artificial neural network using a backpropagation learning algorithm is constructed and the characters are determined.

License Plate Image Generation using Generative Adversarial Networks for End-To-End License Plate Character Recognition from a Small Set of Real Images

“…LP character segmentation methods include pixel connectivity [33], projection profiles [34], prior knowledge of characters [35], character contours [36], and multiple-method binarization schemes [37]. Finally, LPCR methods include using pixel data directly [38], extracted features [39], and neural networks (NNs) [40].…”

A Vehicular License Plate Recognition Framework For Skewed Images

“…CCA (Connected Component Analysis) integrated technique has been implemented as one of the significant methodologies for the processing of the binary images [2,13]. In [14] an algorithm has been implemented for tracking out the connected objects through utilizing the contour detection.…”

“…For Character recognition different attributes i.e. pattern or template matching algorithm [7], implementing extracted attributes [22], deploying classifiers such as Artificial Neural Networks (ANN) [2] or statistical classifiers i.e. Hidden Markov Model (HMM) [23], fuzzy SVM (Support Vector Machine) [24] have been utilized.…”

“…It provides a reference as well for further vehicle activity analysis and tracking. VLPR system has become a core methodology because of its wide range of traffic applications along with security ranging from parking automation to vehicle surveillance, electrical tollgate management, restricted area security control, road traffic monitoring, analysis of vehicle activity, tracking for safety and calculating the traffic volume [1,2].…”

Study of Deep Learning Algorithms for Automatic License Plate Recognition (ALPR)

“…In the current work, we prefer to use an ANN that was been widely applied to pattern recognition. Therefore, in this work [2], a three-layer feedforward ANN that uses a backpropagation learning algorithm is constructed and the characters are determined using this ANN.…”