A robust algorithm for morphological, spatial image-filtering and character feature extraction and mapping employed for vehicle number plate recognition

Mukherjee, Rajshekhar; Pundir, Amit; Mahato, Dharmendra Kumar; Bhandari, Gaurav; Saxena, Geetika Jain

doi:10.1109/wispnet.2017.8299884

Cited by 7 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table V proves that the maximum number of embedded bits has an average difference of 0.2 bpp or the maximum number of message messages that can be embedded increases by 18.65%, with the results of MSE, PSNR and SSIM values averaging 1.13, 47.59 dB, and 0.9977. This value can still be categorized as having a very good imperceptibility aspect because the PSNR result is above 40 dB and the SSIM value is near perfect [21].…”

Section: Experimental and Resultsmentioning

confidence: 81%

“…Edge dilation is widely used in morphological processes aimed at improving the quality of robustness [17]. Basically, the dilation process is performed on binary imagery that aims to enlarge the foreground or white pixel boundary boundary [18], although the dilation process can also be performed on the grayscale image [19]. The dilation process on the edge area of the image makes the image edge area thicker.…”

Section: Edge Dilationmentioning

confidence: 99%

See 1 more Smart Citation

International Journal of Electronics and Telecommunications

Setiadi¹

2019

View full text Add to dashboard Cite

This research proposes a method to enhance the payload message by embedding messages on the dilated edge areas by the Least Significant Bit (LSB) method. To add security aspects to messages, messages are not embedded directly on the LSB but encrypted with XOR operations with Most Significant Bit (MSB). The experimental results of the test in this study showed that the dilation process to some extent can increase the payload of 18.65% and the average bpp is 1.42 while maintaining the imperceptibilty quality of stego image with an average PSNR value of about 47 dB, SSIM is 0.9977 and MSE is 1.13.

show abstract

Section: Experimental and Resultsmentioning

confidence: 81%

Section: Edge Dilationmentioning

confidence: 99%

International Journal of Electronics and Telecommunications

Setiadi¹

2019

View full text Add to dashboard Cite

show abstract

“…The recognition accuracy of their methods was 95.00%. Another fuzzy based license plate region segmentation method was introduced by Mukherjee et al [ 40 ]. They used fuzzy logic to identify edges in the license plate in conjunction with other edge detection algorithms such as Canny and Sobel [ 41 , 42 ].…”

Section: Related Workmentioning

confidence: 99%

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Islam

Raj

Islam

et al. 2020

Sensors

View full text Add to dashboard Cite

Automatic vehicle license plate recognition is an essential part of intelligent vehicle access control and monitoring systems. With the increasing number of vehicles, it is important that an effective real-time system for automated license plate recognition is developed. Computer vision techniques are typically used for this task. However, it remains a challenging problem, as both high accuracy and low processing time are required in such a system. Here, we propose a method for license plate recognition that seeks to find a balance between these two requirements. The proposed method consists of two stages: detection and recognition. In the detection stage, the image is processed so that a region of interest is identified. In the recognition stage, features are extracted from the region of interest using the histogram of oriented gradients method. These features are then used to train an artificial neural network to identify characters in the license plate. Experimental results show that the proposed method achieves a high level of accuracy as well as low processing time when compared to existing methods, indicating that it is suitable for real-time applications.

show abstract

Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)

et al. 2020

View full text Add to dashboard Cite

The present paper proposes a new methodology for license plate (LP) recognition in the state of the art of image processing algorithms and an optimized neutrosophic set (NS) based on genetic algorithm (GA). First of all, we have performed some image processing techniques such as edge detection and morphological operations in order to utilize the (LP) localization. In addition, we have extracted the most salient features by implementing a new methodology using (GA) for optimizing the (NS) operations. The use of (NS) decreases the indeterminacy on the (LP) images. Moreover, k-means clustering algorithm has been applied to segment the (LP) characters. Finally, we have applied connected components labeling analysis (CCLA) algorithm for identifying the connected pixel regions and grouping the appropriate pixels into components to extract each character effectively. Several performance indices have been calculated in order to measure the system efficiency such as accuracy, sensitivity, specificity, dice, and jaccard coefficients. Moreover, we have created a database for all detected and recognized (LP) for testing purposes. Experimental results show that the proposed methodology has the ability to be suitable for both (Arabic-Egyptian) and English (LP). The proposed system achieves high degree of recognition accuracy for the whole system according to the following case studies; (i) for a high resolution Egyptian (LP), the proposed system achieves about 96.67% accuracy of correct recognition, (ii) for a low resolution-corrupted English (LP), the proposed system achieves about 94.27% accuracy. In addition, we have applied the proposed system on some sort of image disturbance i.e. (flash in image, external noise, and illumination variation), the proposed system achieves about 92.5% accuracy of correct identification. However, traditional methods achieve about 79% accuracy of correct identification in the presence of such image degradations. This reflects how the proposed system is generalized, optimized, and proposes high degree of recognition accuracy.

show abstract

A robust algorithm for morphological, spatial image-filtering and character feature extraction and mapping employed for vehicle number plate recognition

Cited by 7 publications

References 4 publications

International Journal of Electronics and Telecommunications

International Journal of Electronics and Telecommunications

A Vision-Based Machine Learning Method for Barrier Access Control Using Vehicle License Plate Authentication

Toward an Optimized Neutrosophic k-Means With Genetic Algorithm for Automatic Vehicle License Plate Recognition (ONKM-AVLPR)

Contact Info

Product

Resources

About