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A Deep Learning Based Approach for Localization and Recognition of Pakistani Vehicle License Plates

License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plate...

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Detalles Bibliográficos
Autores principales: Yousaf, Umair, Khan, Ahmad, Ali, Hazrat, Khan, Fiaz Gul, Rehman, Zia ur, Shah, Sajid, Ali, Farman, Pack, Sangheon, Ali, Safdar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8622297/
https://www.ncbi.nlm.nih.gov/pubmed/34833783
http://dx.doi.org/10.3390/s21227696
Descripción
Sumario:License plate localization is the process of finding the license plate area and drawing a bounding box around it, while recognition is the process of identifying the text within the bounding box. The current state-of-the-art license plate localization and recognition approaches require license plates of standard size, style, fonts, and colors. Unfortunately, in Pakistan, license plates are non-standard and vary in terms of the characteristics mentioned above. This paper presents a deep-learning-based approach to localize and recognize Pakistani license plates with non-uniform and non-standardized sizes, fonts, and styles. We developed a new Pakistani license plate dataset (PLPD) to train and evaluate the proposed model. We conducted extensive experiments to compare the accuracy of the proposed approach with existing techniques. The results show that the proposed method outperformed the other methods to localize and recognize non-standard license plates.