An Efficient Method of Vehicle Number Plate Detection and Recognition

Lazrus, Anish; Choubey, Siddhartha; Gr, Sinha

doi:10.9735/0975-2927.3.3.134-137

Cited by 18 publications

(8 citation statements)

References 3 publications

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“…It filters an image using pixel wise adaptive Wiener filtering, with neighborhoods of size U x V to estimate the local image mean and standard deviation. More details on this algorithm are given in [11].…”

Section: Introductionmentioning

confidence: 99%

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An unequal error protection scheme for SPIHT image transmission with prioritised retransmissions and de-noising

2013

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This paper proposes a robust resilient transmission scheme for images that have been compressed using the Set Partitioning In Hierarchical Trees (SPIHT) algorithm. The scheme employs Unequal Error Protection (UEP), prioritised Automatic Repeat reQuest (ARQ) and twodimensional adaptive Wiener filtering. In order to apply UEP, the SPIHT coded bit-stream is partitioned into bit-classes of unequal importance. UEP is then applied at two levels. Firstly, Rate Compatible Punctured Convolutional (RCPC) codes are used to encode the bit-classes with different code-rates hence providing them with different levels of protection. Secondly, prioritized retransmissions are used such that the more important bit-classes are allocated a greater number of retransmissions than the less important ones. Equal gain combining is also performed to further improve the performance. The proposed UEP scheme provides an average gain of 3 dB in Peak Signal to Noise Ratio (PSNR) over a conventional Equal Error Protection (EEP) in the range 0dB≤Eb/No≤6dB with 16-QAM and an average gain of 3.5 dB in PSNR in the range 0dB≤Eb/No≤3dB with BPSK modulation. Moreover, de-noising with a two dimensional Wiener filter provides an additional gain of 0.76 to 0.90 dB.

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Section: Introductionmentioning

confidence: 99%

“…Two dimensional Wiener filtering is also used to further de-noise the image. The low pass Wiener2 filter de-noises a grayscale image that has been degraded by constant power additive noise [11]. Wiener filtering estimates the local mean and variance around each pixel [12,13] as follows:…”

Section: Introductionmentioning

confidence: 99%

An unequal error protection scheme for SPIHT image transmission with prioritised retransmissions and de-noising

2013

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“…Anish Lazrus et al [18] presented a robust method of license plate location using segmentation and reorganization of the characters present in the located plate. Wiener2 filter was used to remove noise and Sobel filter for finding and smoothing edges and then the connected components were found.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Extract Text From License Plate of Vehicles

R¹

2012

SIPIJ

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In this paper the text found on the vehicle plates is detected from the input image and this requires the localization of number plate area in order to identify the characters present on it. In this work, simple color conversion edge detection and connector measurement technique with the application of median filter as one of the operators is attempted. This paper presents an approach using simple but efficient morphological operations, filtering and finding connected components for localization of Indian number plates. The algorithm has been tested on 100 samples and is found to extract both alphabets and numbers from vehicle license plates images with an accuracy of 93% for both two and four wheeler license plates of Chennai region.

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“…et al [3] presents a powerful method of license plate location, segmentation and recognition of the characters in licence plate. The images of vehicles are converted into gray-scale images.…”

Section: Literature Surveymentioning

confidence: 99%

Comparison of Feed Forward Network and Radial Basis Function for Detecting and Recognition of License Plate

Bagwari¹,

Saxena²,

Bagwari³

et al. 2015

IJCA

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One of the most important topics of intelligent transportation system is the License Plate Recognition (LPR). LPR systems have many potential applications in intelligent traffic systems, such as the payment of parking fee, highway toll fee, traffic data collection, traffic monitoring systems, traffic law enforcement, security control of restricted areas and so on. LPR was developed to identify vehicles by the contents of their license plates. The LPR system consists of three major modules: license plate extraction, segmentation and recognition of individual characters. This paper presents a study of applying the neural network approach for character image recognition. The new approach is tested on 400 samples of extracted license plate images captured in outdoor environment. The result yield 99.2% recognition accuracy, the method takes 1.6 seconds to perform the car plate recognition from vehicle's image. In order to decrease problems such as low quality and low contrast in the vehicle images, image recognition is done by the two different methods first is feed forward method and another is Radial Basis function and the best one is selected. The algorithm based on neural network which can quickly and correctly detect the region of license plate and the license plate detecting rate of success is 99.2%.

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An Efficient Method of Vehicle Number Plate Detection and Recognition

Cited by 18 publications

References 3 publications

An unequal error protection scheme for SPIHT image transmission with prioritised retransmissions and de-noising

An unequal error protection scheme for SPIHT image transmission with prioritised retransmissions and de-noising

A Novel Approach to Extract Text From License Plate of Vehicles

Comparison of Feed Forward Network and Radial Basis Function for Detecting and Recognition of License Plate

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