License Plate Recognition Algorithm for Passenger Cars in Chinese Residential Areas

Abstract

This paper presents a solution for the license plate recognition problem in residential community administrations in China. License plate images are pre-processed through gradation, middle value filters and edge detection. In the license plate localization module the number of edge points, the length of license plate area and the number of each line of edge points are used for localization. In the recognition module, the paper applies a statistical character method combined with a structure character method to obtain the characters. In addition, more models and template library for the characters which have less difference between each other are built. A character classifier is designed and a fuzzy recognition method is proposed based on the fuzzy decision-making method. Experiments show that the recognition accuracy rate is up to 92%.

A smart camera for the surveillance of vehicles in intelligent transportation systems

“…These goals are usually achieved by various binarization methods, including the Otsu binarization [37], adaptive binarization techniques such as variable thresholding [16] or the Sauvola method [2], and other nonadaptive methods, as in [64]. Strengthening of edges is achieved by combining selected binarization methods with techniques including greying, normalizing, histogram equalization, etc., as reported in [41]. Other preprocessing objectives, such as noise removal and general image enhancement, are achieved by applying wavelet-based filters [55] and the top-hat transform [5], respectively.…”

Weighted Domain Transfer Extreme Learning Machine and Its Online Version for Gas Sensor Drift Compensation in E‐Nose Systems

“…The unlabeled samples are organized in a sequence as the left rectangle. The target classifier is initialized with the labeled sample using (4). For the online learning phase, we wish to update the target classifier using current 푇 , the incremental value ℎ, and some intermediate result (s).…”

“…fl the selected samples; fl the unselected samples; (1) Clustering clusters using inner square distance clustering; (2) for each cluster do (3) if the number of samples is less than then (4) Put all the samples into ; (5) else (6) Calculate the distance between samples in this cluster; (7) Selected the two samples with the largest distance and put them in ; (8) Initialize fl 2; (9) while < do (10) Find the nearest distances of the remaining samples to the selected ones; (11) Choose the one with largest distance and put it in ; (12) = + 1; (13) end while (14) Put the unselected samples in ;…”

“…Accordingly, the researches have shifted from original construction and routing problems [1][2][3] to more specific sensory data processing and analyzing tasks [4][5][6]. With the fast development of sensor technology, E-Nose systems comprised of gas sensor arrays have been widely used in air quality monitors, security check points, and other gas compound identification scenarios.…”

Survey on Automatic Vehicle Number Plate Localization

“…The localization was performed based on the number of edge points, the length of license plate area and the number of each line of edge points [20]. The input images were preprocessed by median filter and thereafter by using sobel edge detection the edge points were formed.…”