Mobinur Rahman

Work place: Department of Computer Science and Engineering, American International University-Bangladesh, Dhaka, Bangladesh

E-mail: shanto.siddiq1@gmail.com

Website:

Research Interests: Image Processing, Pattern Recognition, Neural Networks, Computer Vision, Computer systems and computational processes

Biography

Mobinur Rahman is an undergraduate (UG) student of Computer Science and Engineering under the Department of Science and Information Technology of American International University Bangladesh. His research interests and passion are mostly based on Artificial Neural Networks, Computer Vision and Pattern Recognition, Image Processing and Machine Learning.

Author Articles
Real Time Bangla Vehicle Plate Recognition towards the Need of Efficient Model - A Comprehensive Study

By A. F. M. Saifuddin Saif Mohammad Jaber Hossain Md. Hasan Uzzaman Mobinur Rahman MD.Tawhid Islam

DOI: https://doi.org/10.5815/ijigsp.2018.12.04, Pub. Date: 8 Dec. 2018

Bangla vehicle number plate recognition is still an unsolved research issue for efficient investigation of unregistered vehicles, traffic observation, management and most importantly for Intelligent Transportation System (ITS). Previous research on vehicle plate recognition have been suffering various challenges, i.e. capturing high level image from moving vehicles, number plate with complicated background, detection from different tilt and angle, detection in different lightening conditions and weather conditions, recognition of doubtful and ambiguous signs in road time road scenario. The main aim of this research is to provide critical analysis on various perspectives of vehicle plate recognition, i.e. Extraction of vehicle plates from vehicle, Segmentation of characters and finally, Recognition of segmented characters. At first, this research illustrates comprehensive reviews on existing methods. After that, existing frameworks are analyzed based on overall advantages and disadvantages for each steps in the previous research. Finally, extensive experimental validation is depicted in five aspects, i.e. method, accuracy, processing time, datasets and relevancy with real time scenario. Proposed comprehensive reviews are expected to contribute significantly to perform efficient vehicle plate recognition in Intelligent Transportation System (ITS) research.

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