# Traffic Surveillance using Vehicle License Plate Detection and Recognition in Bangladesh

arXiv: https://arxiv.org/abs/2012.02218 · HTML: https://mlsanity.com/paper/2012.02218/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Vehicle and License Plate Recognition with Novel Dataset for Toll Collection](https://mlsanity.com/paper/2202.05631/) (2022)
- [BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition](https://mlsanity.com/paper/2202.12250/) (2022)

## Abstract (arXiv)

Computer vision coupled with Deep Learning (DL) techniques bring out a substantial prospect in the field of traffic control, monitoring and law enforcing activities. This paper presents a YOLOv4 object detection model in which the Convolutional Neural Network (CNN) is trained and tuned for detecting the license plate of the vehicles of Bangladesh and recognizing characters using tesseract from the detected license plates. Here we also present a Graphical User Interface (GUI) based on Tkinter, a python package. The license plate detection model is trained with mean average precision (mAP) of 90.50% and performed in a single TESLA T4 GPU with an average of 14 frames per second (fps) on real time video footage.