International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Double Blind Reviewed

ISSN: 2319-7064




Downloads: 34

Research Paper | Computer Science and Information Technology | India | Volume 10 Issue 3, March 2021


Automated Generation of Challan on Violation of Traffic Rules using Machine Learning

Shubham Kumar Chandravanshi | Hirva Bhagat | Manan Darji | Himani Trivedi


Abstract: Nowadays, people violating traffic rules has become a major issue and the carefree and irresponsible attitude of people is damaging the moral fibre of the society. The traffic regulations in our country have improved leaps and bounds in the last few years but still the human interference aspect in our current system is a liability and leads to mediocre results which could have been much better. This in turns leads to late and sometimes inaccurate delivery of E-challans and papers based challans which in turn encourages such irresponsible drivers. Our proposed uses object detection, machine learning, object tracking and number plate detection techniques to automate the process of picking out traffic offenders (using object detection and object tracking) and generating the E-challans by directly fetching the vehicle information from the RTO after extracting the number plate data ( number plate detection) and deliver the E-challan via Email and SMS on the same day the offence is registered. This will significantly increase the efficiency and accuracy of the system and eliminate the possibility of any human error as there is in the current system


Keywords: Number Plate Detection, Automatic Number Plate Recognition ANPR, Character Segmentation, Character Recognition, Image Processing, Machine Learning, Challan Generation


Edition: Volume 10 Issue 3, March 2021,


Pages: 1157 - 1162


How to Cite this Article?

Shubham Kumar Chandravanshi, Hirva Bhagat, Manan Darji, Himani Trivedi, "Automated Generation of Challan on Violation of Traffic Rules using Machine Learning", International Journal of Science and Research (IJSR), Volume 10 Issue 3, March 2021, pp. 1157-1162, https://www.ijsr.net/get_abstract.php?paper_id=SR21222190144

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