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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 day time. 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 image. 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 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
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