Adaptive Licence Plate Detection Using Hierarchical Attention Based Neural Networks

Adaptive Licence Plate detection(ALPD) is a challenging yet essential task in intelligent transportation systems, with applications ranging from traffic law enforcement to toll collection and parking management. This study proposes a robust approach for ALPD using a hybrid architecture that integrates Convolutional Neural Networks (CNNs) and Optical Character Recognition (OCR) techniques. The proposed method leverages CNNs for efficient feature extraction and localization of license plates, while the OCR module accurately identifies and converts alphanumeric characters into machine-readable text. By combining these components, the architecture addresses challenges such as varying lighting conditions, diverse plate formats, and complex backgrounds, resulting in improved detection and recognition accuracy.