Md. Atikuzzaman

Real-Time Vehicle Number Plate Recognition (ANPR) has been a recurrent subject of research study as a result of many real-world implementations. Yet, numerous of todayâs works are still not Full-bodied and consistent in real-world circumstances and rely on various constraints. Our proposed method to detect and recognize license plates in real-time that is particularly designed to work on videos captured by a camera. It is a distinct approach that is composed of three main phases like plate detection, class letter segmentation, and recognition. These phases are completed by adopting a HAAR Feature-based Classifier to detect license plate, class letter extractor with a proposed method, and Convolution Neural Network for recognizing class letters. Our given method achieved captivating results in our collected dataset. Our dataset composed of 5500 license plates and it achieved a successful recognition rate of 91.38% with approximately 30 frames/second. We evaluate our License Plate Detection system performance with 390 test images and we get 96.92% accuracy and Class Letter Segmentation has achieved 94.61% with the same size of data. We achieved an overall successful recognition rate of 90.90% with real-time performance.

Computer vision-based recognition of Bangle vehicle license plates (LPs) is an arduous task in dirty and muddy situations. This paper proposes an efficient method for real-time computer vision-based recognition of Bangla vehicle LPs using contour analysis and prediction algorithms. The method initially applies gray scaling the input RGB images, histogram equalization to improve the grayscale image quality, edge detection using Sobel edge detector, and adaptive thresholding to convert it to a binary image. The system localizes the vehicle LP based on the maximum rectangular contour area and converts it into a predefined size. Noise removal technique using morphological dilation and erosion operation is used, followed by Gaussian filtering on binary image to improve the image quality further. The system clusters the two-lined LP into seven clusters. The sub-clustering is applied on specific clusters and makes 68 individual sub-clusters. The system extracts vector contour (VC) from each 68 individual classes. After VC extraction, the system normalizes it into a q predefined length. The system applies inter co-relation function (ICF) to categorize each sub-cluster to its previously defined individual class. For that, it calculates the maximum similarity between test and previously trained VCs. The system applies the dependency prediction algorithm in parallel to predict the whole string (district name) in the cluster-1 based on previously categorized class or classes (starting character or characters of the district part). (Metro) or (null) from cluster-2, “-” (hyphen) from cluster-3 and 6 are predicted automatically as their positions are fixed. The system is trained using 68 classes in which each class contains 100 samples generated by the augmentation technique. The system is tested using another set of 68 classes with a total of 68×100=6800 images acquiring the recognition accuracy of 96.62% with the mean computational cost of 8.363ms/f. The system is also tested using 500 vehicle whole Bangla LPs achieving the mean whole LP recognition accuracy of 95.41% with a mean computational cost of 35.803ms/f.