TY  - JOUR
AU  - Haque, A.K.M. Fazlul
AU  - Akter, Nila
AU  - Heya, Shaznina Mahrin
AU  - Khan, Mohammad Fahim Asif
AU  - Akter, Taslima
PY  - 2026
DA  - 2026/02/14
TI  - An Intelligent Smart Parking Framework Using Machine Learning–Based Automatic License Plate Recognition for Enhanced Security
JO  - ICCK Transactions on Mobile and Wireless Intelligence
T2  - ICCK Transactions on Mobile and Wireless Intelligence
JF  - ICCK Transactions on Mobile and Wireless Intelligence
VL  - 2
IS  - 1
SP  - 21
EP  - 30
DO  - 10.62762/TMWI.2026.886184
UR  - https://www.icck.org/article/abs/TMWI.2026.886184
KW  - smart parking
KW  - ALPR
KW  - machine learning
KW  - image processing
KW  - IoT automation
AB  - Urban parking inefficiency has become a critical challenge in modern cities, leading to increased traffic congestion, higher fuel consumption, and greater environmental impact. This paper proposes an intelligent smart parking management system that integrates hardware sensing, machine learning, and computer vision to enable real-time parking monitoring and automated vehicle identification. The system combines infrared sensors, camera modules, and microcontroller-based control with vision-based parking space detection and automatic license plate recognition (ALPR). Experimental results demonstrate that the parking space detection module achieves an accuracy of 93.97%, while the license plate recognition module attains 84.93% accuracy. Extensive testing under real-world conditions confirms the system's reliability and practicality. The proposed approach enhances parking space utilization, reduces parking search time, and offers a scalable, cost-effective foundation for future smart city parking infrastructure.
SN  - 3069-0692
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Haque2026An,
  author = {A.K.M. Fazlul Haque and Nila Akter and Shaznina Mahrin Heya and Mohammad Fahim Asif Khan and Taslima Akter},
  title = {An Intelligent Smart Parking Framework Using Machine Learning–Based Automatic License Plate Recognition for Enhanced Security},
  journal = {ICCK Transactions on Mobile and Wireless Intelligence},
  year = {2026},
  volume = {2},
  number = {1},
  pages = {21-30},
  doi = {10.62762/TMWI.2026.886184},
  url = {https://www.icck.org/article/abs/TMWI.2026.886184},
  abstract = {Urban parking inefficiency has become a critical challenge in modern cities, leading to increased traffic congestion, higher fuel consumption, and greater environmental impact. This paper proposes an intelligent smart parking management system that integrates hardware sensing, machine learning, and computer vision to enable real-time parking monitoring and automated vehicle identification. The system combines infrared sensors, camera modules, and microcontroller-based control with vision-based parking space detection and automatic license plate recognition (ALPR). Experimental results demonstrate that the parking space detection module achieves an accuracy of 93.97\%, while the license plate recognition module attains 84.93\% accuracy. Extensive testing under real-world conditions confirms the system's reliability and practicality. The proposed approach enhances parking space utilization, reduces parking search time, and offers a scalable, cost-effective foundation for future smart city parking infrastructure.},
  keywords = {smart parking, ALPR, machine learning, image processing, IoT automation},
  issn = {3069-0692},
  publisher = {Institute of Central Computation and Knowledge}
}