Automatic Number Plate Recognition System using Connected Component Analysis and Convolutional Neural Network
Sneh Kanwar Singh Sidhu1, Raman Maini2, Dhavleesh Rattan3
1Sneh Kanwar Singh*, Department of Computer Science and Engineering, Punjabi University Patiala (Punjab), India.
2Dr. Raman Maini, Professor Computer Science and Engineering, Punjabi University Patiala (Punjab), India.
3Dr. Dhavlessh Ratan, Assistant Professor, Computer Science and Engineering, Punjabi University Patiala (Punjab), India.
Manuscript received on August 28, 2021. | Revised Manuscript received on October 13, 2021. | Manuscript published on October 30, 2021. | PP: 167-173 | Volume-11 Issue-1, October 2021. | Retrieval Number: 100.1/ijeat.F1636089620 | DOI: 10.35940/ijeat.F1636.1011121
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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: Technology is becoming constantly important for customers. Automatic number plate Recognition (ANPR) is a device which enables the identification of a number plate in real time. For an intelligent car service, ANPR helps to promote growth, customize the classic app and increase consumer and employee productivity. Within the specification, the principal function of ANPR lies of removing the characteristics from an illustration of a license plate. An application that enables customers to display automobile repairs through the license platform number only derived from a loaded picture is augmented by a smart car service. Technological progress is that, so it is thought that improvement is important in this region too, so the best choice for automotive services is a smart car company. This work proposed a methodology to detect the numbers from car license plate using convolutional neural network. In the preprocessing of photographs on license plates, the WLS and FFT filters were included. The images are then fed into the convolutional trainings neural network. On more plates and tests is reported during the testing. Therefore, the findings indicate that the proposed solution can be taken in less time from the license model to accurately identify the characters. The experimental result shows the significance of proposed research by achieving an accuracy of 98% for the localization and true recognition of license plates from the video frames.
Keywords: ANPR, FFT, WLS, CNN, Plate Detection.
Scope of the Article: Pattern Recognition and Analysis