Abstract
Intelligent identification of license plates (LPs) is essential for developing efficient and secure transportation systems. However, recognizing LPs can present a significant challenge given the numerous camera angles, lighting situations, and backgrounds. This research suggests a sequence recognition method for identifying LPs to overcome these difficulties. The formulated approach adjusts the alignment of the LP using a Spatial Transformer Network (STN) and extracts sequence features using an enhanced Convolutional Neural Network (CNN). The extracted features from different CNN layers are combined and given to a bi-directional recurrent neural network (BRNN) for recognition, eliminating the need for character segmentation and accessing the context for the entire image during recognition. This offers the advantage of enabling end-to-end model training on complete LP images. The system was evaluated using a collection of data that includes annotations for a complex set of LP images from various scenes and acquisition scenarios. The experiment outcome illustrates that, in comparison to current techniques, our formulated framework achieves adequate recognition accuracy.
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Acknowledgement
This work was supported by the Centre of Excellence in Artificial Intelligence (CoE AI), Veermata Jijabai Technological Institute (VJTI), Mumbai, India.
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Bakshi, A., Udmale, S.S. (2023). ALPR: A Method for Identifying License Plates Using Sequential Information. In: Tsapatsoulis, N., et al. Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science, vol 14184. Springer, Cham. https://doi.org/10.1007/978-3-031-44237-7_27
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DOI: https://doi.org/10.1007/978-3-031-44237-7_27
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