Development of a license plate recognition system for a non-ideal environment

A new algorithm for license plate character recognition system is proposed on the basis of Signature analysis properties and features extraction. Signature analysis has been used to locate license plate region and its properties can be further utilised in supporting and affirming the license plate character recognition. This paper presents the implementation of Signature Analysis combined with Features Extraction to form feature vector for each character with a length of 56. Implementation of these two methods is used in tracking of vehicle’s automatic license plate recognition system (ALPR). The developed ALPR comprises of three phase. The recognition stage utilised the vector to be trained in a simple multi-layer feed-forward back-propagation Neural Network with 56 inputs and 34 neurons in its output layer. The network is trained with both ideal and noisy characters. The results obtained show that the proposed system is capable to recognise both ideal and non-ideal license plate characters. The system also capable to tackle the common character misclassification problems due to similarity in characters.

[1]  Adnan Amin,et al.  Recognition of printed Arabic text using neural networks , 1997, Proceedings of the Fourth International Conference on Document Analysis and Recognition.

[2]  Farrah Wong,et al.  Smearing Algorithm for Vehicle Parking Management System , 2009 .

[3]  Yo-Ping Huang,et al.  A template-based model for license plate recognition , 2004, IEEE International Conference on Networking, Sensing and Control, 2004.

[4]  R. Porter,et al.  Gabor filters for rotation invariant texture classification , 1997, Proceedings of 1997 IEEE International Symposium on Circuits and Systems. Circuits and Systems in the Information Age ISCAS '97.

[5]  Paolo Ferragina,et al.  Optical recognition of motor vehicle license plates , 1995 .

[6]  Brijesh Verma,et al.  A novel feature extraction technique for the recognition of segmented handwritten characters , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[7]  D. Pramadihanto,et al.  Invariant face recognition by Gabor wavelets and neural network matching , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[8]  Kenneth Tze Kin Teo,et al.  Overlapped Vehicle Tracking via Enhancement of Particle Filter with Adaptive Resampling Algorithm , 2020 .

[9]  Muhittin Gökmen,et al.  License Plate Character Segmentation Based on the Gabor Transform and Vector Quantization , 2003, ISCIS.

[10]  Bartosz Wawrzyniak,et al.  License plate localization and recognition in camera pictures , 2002 .

[11]  C. Pornpanomchai,et al.  Printed Thai Character Recognition by Genetic Algorithm , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[12]  Ioannis Anagnostopoulos,et al.  A License Plate-Recognition Algorithm for Intelligent Transportation System Applications , 2006, IEEE Transactions on Intelligent Transportation Systems.

[13]  Farrah Wong,et al.  Tracking and localisation of moving vehicle license plate via signature analysis , 2011, 2011 4th International Conference on Mechatronics (ICOM).

[14]  Yuan F. Zheng,et al.  Object tracking using the Gabor wavelet transform and the golden section algorithm , 2002, IEEE Trans. Multim..

[15]  Sunil V. K. Gaddam,et al.  Efficient Cancelable Biometric Key Generation Scheme for Cryptography , 2010, Int. J. Netw. Secur..

[16]  Kenneth Tze Kin Teo,et al.  Multiple Vehicles License Plate Tracking and Recognition via Isotropic Dilation , 2011, 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks.

[17]  Zehong Yang,et al.  Recognition of gray character using gabor filters , 2002, Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002. (IEEE Cat.No.02EX5997).