# Towards Automatic License Plate Detection
## 2 Related Work  
As briefly reviewed in the following LPD approaches, research on LPD methods has made considerable progress in recent years for the effectiveness of any LPR system. In [7], researchers' developed a novel method to precisely locate and classify characters and license plate regions concurrently. In this work, an assembly layer is introduced for integrating the characters that outputs license plate strings. This method yields encouraging \begin{table}
\begin{tabular}{l l} \hline \hline
**Acronym** & **Meaning** \\ \hline AdaBoost & Adaptive Boosting \\ ALPD & Automatic License Plate Detection \\ AR/CR & Aspect Ratio/Connected Region \\ (\(\psi\)) & Convolutional Feature Map \\ DCNN & Deep Convolutional Neural Networks \\ (\(\oplus\))/(\(\cdot\)) & Dialation/closing operation \\ FP/FN/TP & False Positive/False Negative/True Positive \\ (T) & Feature Vector \\ (\(l\)) & Ground Truth \\ HOG & Histogram of Oriented Gradients \\ HSI/HSV & Hue Saturation Intensity/Value \\ ITS & Intelligent Transportation Systems \\ LPLM & License Plate Localization Module \\ (\(\zeta\)) & Matching Confidence \\ PCA & Principal Component Analysis \\ PKU & Peeking University Dataset \\ RGB & Red, Green, and Blue \\ Rol & Region of Interest \\ RPN & Region Proposal Network \\ SA & Spatial Area \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acronyms and their explanation. results on real world vehicles that contains license plates in different conditions. In [8], the problem of the LPD is tackled by pixels to pixels investigation. Researchers integrated the Adaboost, which is an intelligent classifier with the SIFT-based SVM classifier. In [9], the HOG-based features of a segmented license plate are fed to the Adaboost classifier to locate license plates. In [10], the developed methodology addresses challenges in license plate detection mainly through color transformations and variations in various color spaces through segmentation and filtering procedures. In [11], the proposed approach has three steps to locate a car license plate. Initially, a Sliding Concentric Windows (SCWs)-based methodology is introduced to extract candidate regions, followed by the HSI-color model to identify those candidate regions. In the final stage, the candidate regions are decomposed using a position histogram to figure out characters in the plate. [PERSON] et al. [12] integrated color and texture features to locate complex license plates, whereas [13] proposed a fixed color-pairs-based license plate localization method to process the plate characters and background regions. In [14], a modified template-matching algorithm is presented through color pixel analysis. In [15], a fully Convolutional Neural Network (CNN) was deployed to extract features at different stages of the CNN to differentiate the details of the plates and the background followed by a three loss layer architecture for accurate plate detection. In [16], a YOLO V3 tiny object detector is introduced to detect license plates on images of the high efficiency video coding domain. This work reports a new compressed domain license plate database, which comprises images that are captured by a commercial license plate recognition system. Using at least two-orders-of-magnitude less amount of data, this work reports higher detection results than few of the methods compared therein. Furthermore [17], addressed the license plate detection problem using a Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) in natural scene images. In [18], the work discusses a method to locate Chinese license plates using color detection and segmentation. In [19], a method uses the Faster-RCNN integrated with a hierarchical sampling method to detect the license plate. In [20], license plate detection is achieved through deep learning and character segmentation. In [21], the developed license plate detection technique applies image downscaling and a line density filter approach.