Contributors: MNH Pias, AK Mutasim, MA Amin


A number plate, also known as a license plate is a metal or plastic plate used to uniquely identify vehicles. The objective of an Automatic Number Plate Recognition (ANPR) system is to locate and recognize the number plate of vehicles automatically. An ANPR system can be broken down into three steps: number plate detection, character extraction and character recognition.

There are many algorithms to detect and extract the number plate from the image of a vehicle. For example, Gabor transform, edge detection based algorithms, dynamic programming, AdaBoost, etc. However, there are very few studies for number plate of vehicles registered in Bangladesh containing Bengali (Bangla) alphanumeric characters.

In this project, we are experimenting with state-of-the-art Deep Learning techniques, one of the most popular topics of Machine Learning in the recent past, to automatically detect and recognize number plates from images of vehicles registered in Bangladesh.

Author Contributions

MA Amin conceived the original idea. MNH Pias collected data, carried out the experiments, planned and carried out the simulations with support from AK Mutasim. MNH Pias and AK Mutasim wrote the manuscripts and contributed to the interpretation of the results with input from all the authors. AK Mutasim and MA Amin supervised the project.



  1. Mir Noor-ul Haque Pias, Aunnoy K Mutasim, M. Ashraful Amin, "Bangladeshi Car Number Plate Detection: Cascade Learning vs. Deep Learning", 15th International Workshop on Content-Based Multimedia Indexing (CBMI 2017), 19-21 June, 2017, Firenze, Italy. [More Details]

  2. Mir Noor-ul Haque Pias, Aunnoy K Mutasim, M. Ashraful Amin, "Automatic Detection of Number Plate from Images of Bangladeshi Vehicles", 2017 International Conference on Advances in Artificial Intelligence (ICAAI 2017), 3-5 April, 2017, Bangkok, Thailand. [More Details]



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