In this paper presents license plate recognition system using connected component analysis and template matching model for accurate identification. Automatic license plate recognition (ALPR)
The project presents license plate recognition system using connected component analysis and 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 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.
- Color and character features based 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
License plate recognition system based on Connected Component analysis and Template matching for automatic identification
- Connected component analysis
- Template Matching
- Number Plate Recognition
- Invariant to license plate rotation
- High-performance accuracy
- MATLAB 2014a
 S. Yu, B. Li, Q. Zhang, C. Liu, and M. Q. H. Meng, A novel license plate location method based on wavelet transform and EMD analysis, Pattern Recognition, Vol. 48, Issue. 1, 2015, pp 114-125.
 V. Tadi, M. Popovic, and P. Odry, Fuzzified Gabor filter for license plate detection, Vol. 48, EAAI, 2016, pp 40-58.
 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.
 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.
 K. Suresh, G. M. Kumar, and A. N. Rajagopalan, Superresolution of license plates in real traffic videos, IEEE Trans ITS, Vol. 8, Issue. 2, 2007, pp 321-331.
 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.
 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.  B. Epshtein, E. Ofek, Y. Wexler, Detecting text in natural scenes with stroke width transform. In: Proc. CVPR, 2010, pp 2963-2970.
 N. R. Howe, Document binarization with automatic parameter tuning, IJDAR, Vol. 16, Issue. 3, 2013, pp 247-258.
 Tesseract. http://code.google.com/p/tesseract-ocr/. [