Abstract
Vehicles play a vital role in modern-day transportation systems. Number plate provides a standard means of identification for any vehicle. To serve this purpose, automatic license plate recognition system was developed. This consisted of four major steps: preprocessing of obtained image, extraction of license plate region, segmentation, and character recognition. In earlier research, direct application of Sobel edge detection algorithm or applying threshold was used as key steps to extract the license plate region, which do not produce efficient results when captured image is subjected to high intensity of light. The use of morphological operations causes deformity in the characters during segmentation. We propose a novel algorithm to tackle the mentioned issues through a unique edge detection algorithm. It is also a tedious task to create and update the database of required vehicles frequently. This problem is solved by the use of ‘Internet of things’ where an online database can be created and updated from any module instantly. Also, through IoT, we connect all the cameras in a geographical area to one server to create a ‘universal eye’ which drastically increases the probability of tracing a vehicle over having manual database attached to each camera for identification purpose.
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References
Kim, S.K., Kim, D.W, Kim, H.J.: A recognition of vehicle license plate using a genetic algorithm based segmentation. In: Image Processing Proceedings, vol. 2, pp. 661–664. IEEE (1996)
Arth, C., Limberger, F., Bischof, H.: Real-time license plate recognition on an embedded DSP-platform. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR’07, pp. 1–8, June 2007
Ozbay, S., Ercelebi, E.: Automatic vehicle identification by plate recognition. World Acad. Sci. Eng. Technol. 1, 1410–1413 (2007)
Sulehria, H.K., Ye Zhang, D.I., Sulehria, A.K.: Vehicle number plate recognition using mathematical morphology and neural networks. WSEAS Trans. Comput. 7, 781–790 (2008)
Qadri, M.T., Asif, M.: Automatic number plate recognition system for vehicle identification using optical character recognition. In: ICETC’09, pp. 335–338. IEEE (2009)
Roy, A., Ghoshal, D.P.: Number Plate Recognition for use in different countries using an improved segmentation. In: NCETACS, pp. 1–5. IEEE, Mar 2011
Arulmozhi, K., Perumal, S.A., Priyadarsini, C.T., Nallaperumal, K.: Image refinement using skew angle detection and correction for Indian license plates. In: ICCIC, pp. 1–4. IEEE, Dec 2012
Kaur, A., Jindal, S., Jindal, R.: License plate recognition using support vector machine (SVM). Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2–7 (2012)
Roy, S., Choudhury, A., Mukherjee, J.: An approach towards detection of Indian number plate from vehicle. Int. J. Innov. Technol. Explor. Eng. 2, 2–4 (2013)
Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23, 311–325 (2013)
Hsu, G.S., Chen, J.C., Chung, Y.Z.: Application-oriented license plate recognition. IEEE Trans. Veh. Technol. 2, 552–561 (2013)
Sulaiman, N, Jalani, S.N.H.M., Mustafa, M., Hawari, K.: Development of automatic vehicle plate detection system. In: IEEE 3rd International Conference on System Engineering and Technology, pp. 130–135. Aug 2013
Bhat, R., Mehandia, B.: Recognition of vehicle number plate using matlab. Int. J. Innov. Res. Electr. Electron. Instrum. 2, 2–8 (2014)
Kaur, S.: An efficient approach for number plate extraction from vehicles. Int. J. Comput. Sci. Inf. Technol. 5, 2954–2959 (2014)
Chuang, C.H., Tsai, L.W., Deng, M.S., Hsieh, J.W., Fan, K.C.: Vehicle licence plate recognition using super-resolution technique. In: 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 411–416. IEEE (2014)
Sharma, J., Mishra, A., Saxena, K., Kumar, S.: A hybrid technique for license plate recognition based on feature selection of wavelet transform and artificial neural network. In: Optimization, Reliabilty and Information Technology (ICROIT), pp. 347–352 (2014)
Rabee, A., Barhumi, I.: License plate detection and recognition in complex scenes using mathematical morphology and support vector machines. In: Systems, Signals and Image Processing (IWSSIP), pp. 59–62. IEEE (2014)
Prabhakar, P., Anupama, P., Resmi, S.R.: Automatic vehicle number plate detection and recognition. In: Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on (pp. 185–190). IEEE (2014, July)
Raskar, R.R., Dabhade, R.G.: Automatic number plate recognition (ANPR). Int. J. Emerg. Technol. Adv. Eng. 5, (2015)
García-Sánchez, S., Aubert, S., Iraqui, I., Janbon, G., Ghigo, J.M., d’Enfert, C.: Candida albicans biofilms: a developmental state associated with specific and stable gene expression patterns. Eukaryot. Cell 3(2), 536–545 (2004)
Dewan, S., Bajaj, S., Prakash, S.: Using Ant’s Colony Algorithm for improved segmentation for number plate recognition. In: Computer and Information Science (ICIS), 2015 IEEE/ACIS 14th International Conference on (pp. 313–318). IEEE. 2 (2015, June)
Kapoor, A., Bhat, S.I., Shidnal, S., Mehra, A.: Implementation of IoT (Internet of Things) and Image processing in smart agriculture. In: Computation System and Information Technology for Sustainable Solutions (CSITSS), International Conference on (pp. 21–26). IEEE (2016, October)
Khan, J.A., Shah, M.A.: Car Number Plate Recognition (CNPR) system using multiple template matching. In: Automation and Computing (ICAC), 2016 22nd International Conference on (pp. 290–295). IEEE (2016, September)
Babu, K.M., Raghunadh, M.V.: Vehicle number plate detection and recognition using bounding box method. In: Advanced Communication Control and Computing Technologies (ICACCCT), 2016 International Conference on (pp. 106–110). IEEE (2016, May)
Agarwal, A., Goswami, S.: An efficient algorithm for automatic car plate detection & recognition. In: Computational Intelligence & Communication Technology (CICT), 2016 Second International Conference on (pp. 644–648), IEEE (2016, February)
Acknowledgements
I am thankful to my university, Vellore Institute of Technology, for providing a wonderful platform to learn and work out our ideas into pragmatic projects. I am also thankful to Dr. Rajesh Kumar Muthu, for his valuable guidance, encouragement, and cooperation during the course of the project. The images tested during the experimentation were from various online sources and personally created database to check the efficiency of the proposed algorithm, but the images displayed in the paper are all from online sources (Google) and publicly available images. I, the first author (Tejas K), would like to state that I take the sole responsibility in the future for the images published in this paper.
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Tejas, K., Ashok Reddy, K., Pradeep Reddy, D., Bharath, K.P., Karthik, R., Rajesh Kumar, M. (2019). Efficient License Plate Recognition System with Smarter Interpretation Through IoT. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 817. Springer, Singapore. https://doi.org/10.1007/978-981-13-1595-4_16
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DOI: https://doi.org/10.1007/978-981-13-1595-4_16
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