No abstract

Tilt correction is an essential step in the license plate recognition system (LPR). The main goal of this article is to provide a review of the various methods that are presented in the literature and used to correct different types of tilt that appear in the digital image of the license plates (LP). This theoretical survey will enable the researchers to have an overview of the available implemented tilt detection and correction algorithms. That’s how this review will simplify for the researchers the choice to determine which of the available rotation correction and detection algorithms to implement while designing their LPR system. This review also simplifies the decision for the researchers to choose whether to combine two or more of the existing algorithms or simply create a new efficient one. This review doesn’t recite the described models in the literature in a hard-narrative tale, but instead, it clarifies how the tilt correction stage is divided based on its initial steps. The steps include: locating the plate corners, finding the tilting angle of the plate, then, correcting its horizontal, vertical, and sheared inclination. For the tilt correction stage, this review clarifies how state-of-the-art literature handled each step individually. As a result, it has been noticed that line fitting, Hough transform, and Randon transform are the most used methods to correct the tilt of a LP.

A signature is a special identifier that confirms a person's identity and distinguishes him or her from others. The main goal of this paper is to present a deep study of the spatial density distribution method and the effect of a mass-based segmentation algorithm on its performance while it is being used to recognize handwritten signatures in an offline mode. The methodology of the algorithm is based on dividing the image of the signature into tiles that reflect the shape and geometry of the signature, and then extracting five spatial features from each of these tiles. Features include the mass of each tile, the relative mean, and the relative standard deviation for the vertical and horizontal projections of that tile. In the classification stage, four measurements of the Euclidean distance were used. While the accuracy rates for 4854 samples drawn from five different evaluated standard datasets ranged from 92.24% to 100%.