Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System

The number plate recognition system must be able to quickly and accurately identify the plate in both low and noisy lighting conditions, as well as within the specified time limit. This study proposes automated authentication, which would minimize security and individual workload while eliminating the requirement for human credential verification. The four processes that follow the acquisition of an image are pre-processing, number plate localization, character segmentation, and character identification. A human error during the affirmation and the enrolling process is a distinct possibility since this is a manual approach. Personnel at the selected location may find it difficult and time-consuming to register and compose information manually. Due to the printed edition design, it is impossible to communicate the information. Character segmentation breaks down the number plate region into individual characters, and character recognition detects the optical characters. Our approach was tested using genuine license plate images under various environmental circumstances and achieved overall recognition accuracy of 91.54% with a single license plate in an average duration of 2.63 seconds.

@article{Naidu2022,
  title     = {Character Level Segmentation and Recognition using CNN Followed Random Forest Classifier for NPR System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {11},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {U. Ganesh Naidu and R. Thiruvengatanadhan and S. Narayana and P. Dhanalakshmi},
  doi       = {10.14569/IJACSA.2022.0131102},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131102}
}