Multileveled ALPR using Block-Binary-Pixel-Sum Descriptor and Linear SVC

Automatic license plate recognition (ALPR) is es-sential component of security and surveillance. ALPR mainly aims to detect and prevent the crime and fraud activities; it also plays an important role in traffic monitoring. An algorithm is proposed for recognizing license plate candidates. The proposed work aimed to recognize the license plate of a car. Proposed work is designed in multilevel for more accurate License Plate (LP) recognition, At level 1 algorithm produced 93.5% accuracy and in level 3 algorithm gives 96% accuracy. For training and testing purpose, LP images were used from Medialab cars dataset, kaggle car dataset and goggle map images. These images in the dataset is formulated at various angles and illumination. Proposed algorithm for LP recognition is done by using the Block Binary Pixel descriptors (BBPS) and Linear Support Vector Classification (SVC). Proposed algorithm is novel and produces higher accuracy in minimal processing time of an average 0.42 milliseconds with 96% accuracy when compared with state-of-the art methods.

@article{Lavanya2022,
  title     = {Multileveled ALPR using Block-Binary-Pixel-Sum Descriptor and Linear SVC},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {5},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {B. Lavanya and G. Lalitha},
  doi       = {10.14569/IJACSA.2022.0130591},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130591}
}