Description
Non Orthogonal Multiple Access Technique In 5G Communication System
Objective:
This paper presents a license plate recognition system using connected component analysis and a template matching model for accurate identification. Automatic license plate recognition (ALPR)
Non Orthogonal Multiple Access Technique In 5G Communication System
ABSTRACT:
The project presents a license plate recognition system using connected component analysis and a template matching model for accurate identification. Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image. The system model uses already captured images for this recognition process. First, the recognition system starts with character identification based on number plate extraction, Splitting characters, and template matching. ALPR as a real-life application has to quickly and successfully process license plates under different environmental conditions, such as daytime. It plays an important role in numerous real-life applications, such as automatic toll collection, traffic law enforcement, parking lot access control, and road traffic monitoring. The system uses different templates for identifying the characters from input images. After character recognition, an identified group of characters will be compared with database number plates for authentication. The proposed model has low complexity and is less time-consuming in terms of number plate segmentation and character recognition. This can improve the system performance and make the system more efficient by taking relevant samples. Non Orthogonal Multiple Access Technique In 5G Communication System
Non Orthogonal Multiple Access Technique In 5G Communication System
Existing method:
- Color and character features based on license plate extraction
- Texture based segmentation
Drawbacks of existing:
- Sensitive to noise and it may generate an error during detection
- Computationally complex and Time-consuming
Non Orthogonal Multiple Access Technique In 5G Communication System
Proposed method:
License plate recognition system based on Connected Component analysis and Template matching for automatic identification
Methodologies
- Segmentation
- Connected component analysis
- Template Matching
- Number Plate Recognition
Advantages:
- Flexibility
- Invariant to license plate rotation
- High-performance accuracy
SOFTWARE REQUIREMENT:
- MATLAB 2014a
REFERENCES:
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[3] J. Tian, R. Wang, G. Wang, J. Liu, and Y. Xia, A two-stage character segmentation method for Chinese license plate, Computers and Electrical Engineering, Vol. 46, 2015, pp 539-553.
[4] C. N. E. Anagnostopoulos, L. E. Anagnostopoulos, V. Loumos and E. Kayafas, A license plate-recognition algorithm for intelligent transportation system applications, IEEE Trans. ITS, Vol. 7, Issue. 3, 2006, pp 377-392.
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[6] S. Du, M. Ibrahim, M. Shehata, and W. Badawy, Automatic license plate recognition (ALPR): A state-of-the-art review, IEEE Trans. CSVT, Vol. 23, Issue. 2, 2013, pp 311-325.
[7] V. Khare. P. Shivakumara, P. Raveendran, L. K. Meng, and H. H. Woon, A new sharpness based approach for character segmentation in license plate images, In Proc. ACPR, 2015, pp. 544-548. [8] B. Epshtein, E. Ofek, Y. Wexler, Detecting text in natural scenes with stroke width transform. In: Proc. CVPR, 2010, pp 2963-2970.
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[10] Tesseract. http://code.google.com/p/tesseract-ocr/. [
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