Abstract
Detecting multiple license plate numbers is crucial for vehicle tracking and re-identification. The reliable detection of multiple license plate numbers requires addressing the challenges like image defocusing and varying environmental conditions like illumination, sunlight, shadows, weather conditions etc. This paper aims to develop a new approach for multiple license plate number detection of different vehicles in day and night scenarios. The proposed work segments the vehicle region containing license plate numbers based on a multi-column convolutional neural network and iterative clustering to reduce the background challenges and the presence of multiple vehicles. To address challenges of font contrast variations and text-like objects in the background, the proposed work introduces the Gaussian kernels that represent a text pixel distribution to integrate with a proposed deep learning model for detection, Experimental results on benchmark datasets of day and night license plate number show that the proposed model is effective and outperforms the existing methods.
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This work is partially supported by IDEAS-Technology Innovation Hub grant, Indian Statistical Institute, Kolkata, India.
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Das, S., Shivakumara, P., Pal, U., Ramachandra, R. (2023). Gaussian Kernels Based Network for Multiple License Plate Number Detection in Day-Night Images. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14191. Springer, Cham. https://doi.org/10.1007/978-3-031-41734-4_5
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