Feasibility of Efficient Number Plate Recognition using Morphological Dilation and Support Vector Machine
SArun Vaishnav1, B. L. Ahuja2, Manju Mandot3
1Arun Vaishnav*, Department of Computer Science, Mohanlal Sukhadia University, Udaipur (Raj), India. Prof.
2 B. L. Ahuja, Department of Physics, University College of Science, Mohanlal Sukhadia University, Udaipur (Raj) , India.
3Prof. Manju Mandot, Department of Computer Science & IT, Janardan Rai Nagar Rajasthan Vidhyapeeth (Deemed-to-be university), Udaipur (Raj), India.
Manuscript received on 12 August 2019. | Revised Manuscript received on 27 August 2019. | Manuscript published on 30 September 2019. | PP: 345-350 | Volume-8 Issue-3 September 2019 | Retrieval Number: C4161098319/19©BEIESP | DOI: 10.35940/ijrte.C4161.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intelligent data acquisition of vehicle number plate plays a significant role to recognize a vehicle and it automatic parking, traffic movement and scheduling, tracking of stolen vehicle, and many more. Although different methodologies of automatic number plate reading have developed alongwith their algorithms, still an efficient number plate recognition technique for better segmentation and recognition of the captured number plate using Morphological Dilation and Support Vector Machine (SVM) are expected to be helpful. In this paper, we present a modified method for recognition of contents of number plate using morphological dilation and SVM. We have compared our results with those from the existing models using Wavelet Transform and Artificial Neural Network techniques. Superiority of present methodology is established using parameters like image segmentation and recognition.
Keywords: Automatic Number Plate Recognition, Morphological Dilation, Support Vector Machine (SVM), Segmentation Rate, Recognition Rate.
Scope of the Article: