Number Plate Detection System Using Computer Vision and Automatic Toll Collection
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The Number Plate Detection and Automatic Toll Collection System is a computer vision-based project designed to automate vehicle identification and toll payment at toll plazas. The system uses Raspberry Pi 4B (4GB RAM) as the central processing unit, powered by a 5V 3A adapter and integrated with an Entry IR Sensor, an Exit IR Sensor, a Logitech C270 USB Camera (1280×720 resolution), a Servo Motor, an I2C 16x2 LCD Display, and an LED Indicator. When a vehicle approaches, the Entry IR Sensor triggers the USB Camera to capture the vehicle’s front image. OpenCV is used for number plate localisation and image pre-processing, and Tesseract OCR (configured with Page Segmentation Mode 8 and the LSTM engine) extracts the vehicle’s registration number from the cropped plate region. The detected number plate is first displayed on the web interface before any gate action is taken. The system then queries a three-table MySQL/SQLite database for owner details and applies the Haversine formula to GPS coordinates to determine if the owner resides within 10 km of the toll plaza; if so, the toll is waived, otherwise it is automatically deducted. The Servo Motor lifts the gate barrier, the I2C 16x2 LCD displays the transaction status, and the LED Indicator and Buzzer provide real-time feedback. The Exit IR Sensor detects vehicle departure and triggers gate closure. The system achieves Tesseract OCR precision of 90.81% and recall of 83.97%, with end-to-end operation completing in under 2 seconds per vehicle, significantly reducing manual intervention and enabling smart contactless toll operations.
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M. Fang, C. Liang, and X. Zhao, “A method based on rough set and SOFM neural network for car plate character recognition,” in Proc. WCICA 2004, vol. 5, pp. 4037–4040, IEEE.
N. A. Jusoh, J. M. Zain, and T. A. A. Kadir, “Enhancing thinning method for Malaysian car plates recognition,” in Proc. ICICIC’07, 2nd Int. Conf., pp. 378–378, IEEE, 2007.
E. R. Lee, P. K. Kim, and H. J. Kim, “Automatic recognition of a car license plate using color image processing,” in Proc. ICIP-94, vol. 2, pp. 301–305, IEEE, 1994.
P. Hurtik and M. Vajgl, “Automatic license plate recognition in difficult conditions — Technical report,” in Proc. IFSA-SCIS 2017, pp. 1–6.
S. Du, M. Ibrahim, M. Shehata, and W. Badawy, “Automatic License Plate Recognition (ALPR): A State-of-the-Art Review,” IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 2, pp. 311–325, Feb. 2013.
P. Swaroop and N. Sharma, “An Overview of Various Template Matching Methodologies in Image Processing,” Int. J. Comput. Appl., vol. 153, no. 10, Nov. 2016.
L. Kodwani and S. Meher, “Automatic License Plate Recognition in Real Time Videos using Visual Surveillance Techniques,” ISSN 2320-8945, vol. 1, no. 6, 2013.
R. Islam, K. F. Sharif, and S. Biswas, “Automatic Vehicle Number Plate Recognition Using Structured Elements,” in Proc. IEEE SPCCON, Dec. 2015, pp. 44–48.
M. T. Qadri and M. Asif, “Automatic Number Plate Recognition System for Vehicle Identification using Optical Character Recognition,” in Proc. IEEE, 2009.
A. Puranic, D. K. T, and U. V, “Vehicle Number Plate Recognition System: A Literature Review and Implementation using Template Matching,” Int. J. Comput. Appl., vol. 134, no. 1, Jan. 2016.
B.-G. Han, J. T. Lee, K.-T. Lim, and Y. Chung, “Real-Time License Plate Detection in High Resolution Videos Using Fastest Available Cascade Classifier and Core Patterns,” ETRI J., vol. 37, no. 2, Apr. 2015.
M. M. Shidore and S. P. Narote, “Number Plate Recognition for Indian Vehicles,” IJCSNS, vol. 11, no. 2, Feb. 2011.
A. O. Agbeyangi, O. A. Alashiri, and A. E. Otunuga, “Automatic Identification of Vehicle Plate Number Using Raspberry Pi,” in Proc. IMCECS, Abeokuta, Nigeria, pp. 1–8, 2020.
A. Firasanti et al., “License Plate Detection Using OCR Method with Raspberry Pi,” in Proc. IEEE Conf. Elect. Eng., Bekasi, Indonesia, pp. 1–6, 2021.
R. R. Palekar et al., “Real Time License Plate Detection Using OpenCV and Tesseract,” in Proc. ICCSP, India, pp. 2111–2114, 2017.
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