https://opus.lib.uts.edu.au/bitstream/10453/4018/4/2006014691.pdf

Region-based license plate detection

In this paper, an Iranian vehicle license plate recognition system based on a new localization approach, which is modified to reflect the local context, is proposed, along with a hybrid classifier that recognizes license plate characters. The method presented here is based on a modified template-matching technique by the analysis of target color pixels to detect the location of a vehicle's license plate. A modified strip search enables localization of the standard color-geometric template utilized in Iran and several European countries. This approach uses periodic strip search to find the hue of each pixel on demand. In addition, when a group of target pixels is detected, it is analyzed to verify that its shape and aspect ratio match those of the standard license plate. In addition to being scale and rotation invariant, this method avoids time-consuming image algorithms and transformations for the whole image pixels, such as resizing and Hough, Fourier, and wavelet transforms, thereby cutting down the detection response time. License plate characters are recognized by a hybrid classifier that comprises a decision tree and a support vector machine with a homogeneous fifth-degree polynomial kernel. The performance detection rate and the overall system performance achieved are 96% and 94%, respectively.

An Iranian License Plate Recognition System Based on Color Features

In this paper, a video processing methodology for a field-programmable gate array (FPGA)-based license plate recognition (LPR) system is researched. The raster scan video is used as an input with low memory utilization. During the design, Gabor filter, threshold, and connected component labeling (CCL) algorithms are used to obtain license plate region. This region is segmented into disjoint characters for the character recognition phase, where the self-organizing map (SOM) neural network is used to identify the characters. The system is portable and relatively faster than computer-based recognition systems. The robustness of the system has been tested with a large database acquired from parking lots and a highway. The memory requirements are uniquely designed to be extremely low, which enables usage of smaller FPGAs. The resulting hardware is suitable for applications where cost, compactness, and efficiency are system design constraints.

Efficient Embedded Neural-Network-Based License Plate Recognition System

Vehicle license plate (VLP) recognition is an interesting problem that has attracted many computer vision research groups. One of the most important and difficult tasks of this problem is VLP detecting. It is not only used in VLP recognition systems but also useful for many traffic management systems. Our method is used for a VLP recognition system that deals with Vietnamese VLPs and it can also be applied to other types of VLPs with minor changes. There are various approaches to this problem, such as texture-based, morphology-based and boundary line-based. In this paper, we present the boundary line-based method that optimizes speed and accuracy by combining the Hough transform and contour algorithm. The enhancement of applying the Hough transform to contour images is the much improved speed of the algorithm. In addition, the algorithm can be used on VLP images that have been taken from various distances and have inclined angles between /spl plusmn/30/spl deg/ from the camera. Especially, it can detect plates in images which have more than one VLP. The algorithm was evaluated in two image sets with an accuracy of about 99%.

Combining Hough transform and contour algorithm for detecting vehicles' license-plates

This paper describes a plate recognition system that can process images rapidly at high accuracy rates. This system is designed to meet the requirements of performance, computational speed, and adaptation for vehicle surveillance applications, such as stolen car detection systems. These requirements are satisfied by adopting a cascade framework, utilizing plate characteristics, and developing fast one-pass algorithms. Our system is composed of three main cascading modules for plate detection, character segmentation, and postprocessing. Each module is further decomposed into several cascading procedures, which are composed of successively more complex rejecters. The first module rapidly rejects a majority of nonplate regions by using low computational gradient features and a one-pass scanning algorithm followed by heavy computational statistical rejecters. The second module rejects a majority of noncharacter regions in a similar manner. A peak-valley analysis algorithm is proposed to rapidly detect all promising candidates of character regions. The third module eliminates the plate characters that do not satisfy the plate specifications. In our experiments, the system can recognize plates over 38 frames per second with a resolution of 640 times 480 pixels on a 3-GHz Intel Pentium 4 personal computer

https://ir.nctu.edu.tw:443/bitstream/11536/10750/1/000246740700014.pdf

A Cascade Framework for a Real-Time Statistical Plate Recognition System

In recent years, the method of license plate tracking has been applied for obtaining the position of the nearby vehicles from a vehicle. In this paper, we propose a new method of license plate extracting and tracking from time sequential images taken by a video camera on a vehicle. For extracting and tracking the plate correctly, we use Hough transform and Voted Block Matching. This method enables the extracting and tracking even when the distance between the camera on the vehicle and the license plate of another vehicle is changing or the plate is occluded by some objects.

H. Saji

Papers

Extraction and tracking of the license plate using Hough transform and voted block matching