Color, Scale, and Rotation Independent Multiple License Plates Detection in Videos and Still Images

Abstract

Most of the existing license plate (LP) detection systems have shown significant development in the processing of the images, with restrictions related to environmental conditions and plate variations. With increased mobility and internationalization, there is a need to develop a universal LP detection system, which can handle multiple LPs of many countries and any vehicle, in an open environment and all weather conditions, having different plate variations. This paper presents a novel LP detection method using different clustering techniques based on geometrical properties of the LP characters and proposed a new character extraction method, for noisy/missed character components of the LP due to the presence of noise between LP characters and LP border. The proposed method detects multiple LPs from an input image or video, having different plate variations, under different environmental and weather conditions because of the geometrical properties of the set of characters in the LP. The proposed method is tested using standard media-lab and Application Oriented License Plate (AOLP) benchmark LP recognition databases and achieved the success rates of 97.3% and 93.7%, respectively. Results clearly indicate that the proposed approach is comparable to the previously published papers, which evaluated their performance on publicly available benchmark LP databases.

License number plate recognition system using entropy‐based features selection approach with SVM

“…As a future work, we will add few more set of features and will implement improved features selection technique to reduce the error rate and increase the recognition rate. Chen et al [8] 2009 93.10 Wen et al [5] 2011 97.88 Kasaei et al [27] 2011 98.20 Zheng et al [19] 2012 98.00 Rasheed et al [16] 2012 90.62 Dehshibi et al [28] 2012 94.50 Cinsdikici et al [15] 2013 92.00 Azad and Shayegh [18] 2013 98.66 Gou et al [20] 2014 97.90 Rabee and Barhumi [29] 2014 97.89 Rajput et al [30] 2015 96.40 Xing et al [31] 2016 95.00 Panahi and Gholampour [32] 2016 97.60 proposed -99.50 [34] 2010 90.9 Psyllos et al [26] 2012 94.6 Hsu et al [35] 2013 92.1 Shahraki et al [36] 2013 84 Smara and Khalefah [37] 2014 97.61 Davis and Arunvinodh [38] 2015 92 Soora and Deshpande [39] 2016 97.56 proposed -99.30…”

Locating multiple license plates using scale, rotation, and colour‐independent clustering and filtering techniques

“…17, we eliminate a significant number of non‐LP components which helps reduce the number of components which need to be checked for LP character matches. Table 2 provides a comparison between the method in [22] and the proposed approach in removing non‐LP components from the input image.…”

“…However, with additional tunings of the threshold values, higher results can be obtained by the proposed method for a specific dataset. The clustering method from [22] reports a LP detection success rate of 97.3% for the Media‐lab and 93.7% for the AOLP dataset. However due to the large number of non‐LP components that are retained [22] will have a lower rate of accurately locating the LP, unfortunately the authors have not made the location rate available.…”

“…The method while not specific to LP detection classified similar objects together using a clustering based approach. Soora and Deshpande [22] proposed a method based on a clustering technique applied to geometrical properties of the connected components in an image. The method used a thinning and resizing operation to differentiate between the LP and non‐LP components and then detects the LP.…”

Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)

“…In order to evaluate the accuracy of the proposed method for different LP variations on popular and publicly available benchmark datasets [43], we have used Media Lab benchmark LP data set [44] and AOLP benchmark LP data sets [45]. We have added some important and critical case studies according to Media Lab benchmark LP data set as shown in Table 22, and also AOLP benchmark LP data sets as shown in Table 23.…”