Character Recognition for ALPR Systems: A New Perspective

The automatic license plate recognition (ALPR) systems are utilized to locate vehicles’ license (or number) plates and extract the information it contains from the image or video. The paper presents a new method of computing the recognition efficiency in which a successful recognition is only considered if the whole license plate is correctly recognized instead of focusing on individual characters, as it is more useful to consider the license plate as a whole. The recognition efficiency of template matching algorithm and SVM-based feature matching algorithm was determined to be 76.36% and 80%, respectively.

[1]  Boubakeur Boufama,et al.  Automatic license plate recognition: A comparative study , 2015, 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT).

[2]  Pawan Kumar Dahiya,et al.  A Review of Recognition Technique Used Automatic License Plate Recognition System , 2015 .

[3]  Lianwen Jin,et al.  A New CNN-Based Method for Multi-Directional Car License Plate Detection , 2018, IEEE Transactions on Intelligent Transportation Systems.

[4]  Orhan Bulan,et al.  Segmentation- and Annotation-Free License Plate Recognition With Deep Localization and Failure Identification , 2017, IEEE Transactions on Intelligent Transportation Systems.

[5]  Rahim Panahi,et al.  Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications , 2017, IEEE Transactions on Intelligent Transportation Systems.

[6]  Yun Yang,et al.  Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features , 2017 .

[7]  Pawan Kumar Dahiya,et al.  A Review of Recognition Techniques in ALPR Systems , 2017 .

[8]  Mahmood Fathy,et al.  Ieee Transactions on Intelligent Transportation Systems 1 an Iranian License Plate Recognition System Based on Color Features , 2022 .

[9]  Nikos Komodakis,et al.  A Robust and Efficient Approach to License Plate Detection , 2017, IEEE Transactions on Image Processing.

[10]  Qi Li,et al.  A Geometric Framework for Rectangular Shape Detection , 2014, IEEE Transactions on Image Processing.

[11]  Wael Badawy,et al.  Automatic License Plate Recognition (ALPR): A State-of-the-Art Review , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Sajid Hussain,et al.  Multiple licence plate detection for Chinese vehicles in dense traffic scenarios , 2016 .

[13]  Seok-Bum Ko,et al.  Improved License Plate Localization Algorithm Based on Morphological Operations , 2018 .

[14]  G. Abo Samra,et al.  Localization of License Plate Number Using Dynamic Image Processing Techniques and Genetic Algorithms , 2014, IEEE Transactions on Evolutionary Computation.

[15]  Palaiahnakote Shivakumara,et al.  Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition , 2018, IEEE Transactions on Circuits and Systems for Video Technology.

[16]  Hong Yan,et al.  Combining Region-of-Interest Extraction and Image Enhancement for Nighttime Vehicle Detection , 2016, IEEE Intelligent Systems.

[17]  Bo Li,et al.  Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance , 2014, IEEE Transactions on Intelligent Transportation Systems.