DELP-DAR System for License Plate Detection and Recognition
Zied Selmi, Mohamed Ben Halima, Umapada Pal, M.Adel Alimi

TL;DR
This paper introduces an automatic license plate detection and recognition system using mask region convolutional neural networks, capable of handling complex scenes, multiple languages, and various environmental conditions with high robustness.
Contribution
The study presents a unified framework employing Mask R-CNN for license plate detection, segmentation, and recognition across diverse datasets and challenging real-world scenarios.
Findings
Robust performance across four diverse datasets.
Effective handling of multiple languages and complex backgrounds.
High accuracy in varied environmental conditions.
Abstract
Automatic License Plate detection and Recognition (ALPR) is a quite popular and active research topic in the field of computer vision, image processing and intelligent transport systems. ALPR is used to make detection and recognition processes more robust and efficient in highly complicated environments and backgrounds. Several research investigations are still necessary due to some constraints such as: completeness of numbering systems of countries, different colors, various languages, multiple sizes and varied fonts. For this, we present in this paper an automatic framework for License Plate (LP) detection and recognition from complex scenes. Our framework is based on mask region convolutional neural networks used for LP detection, segmentation and recognition. Although some studies have focused on LP detection, LP recognition, LP segmentation or just two of them, our study uses the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
