The character segmentation is done by connected component analysis and boundary box analysis and finally in the last character recognition phase, the characters are recognized by matching with the template database using correlation and output results are displayed. So the extracted plate area is enhanced by using morphological operations to improve the quality of extracted plate so that the segmentation phase gives more successful output. Sometimes the extracted plate area also contains noise, bolts, frames etc. To overcome these drawbacks I proposed an efficient approach for ANPR in which the input vehicle image is pre-processed firstly by iterative bilateral filtering, adaptive histogram equalization and number plate is extracted from pre-processed vehicle image using morphological operations, image subtraction, image binarization/thresholding, sobel vertical edge detection and by boundary box analysis. Due to wrong extraction of number plate area, the character segmentation and character recognition are also not successful in this case by using the existing method. The existing methods of ANPR works well for dark and bright/light categories image but it does not work well for Low Contrast, Blurred and Noisy images and the detection of exact number plate area by using the existing ANPR approach is not successful even after applying existing filtering and enhancement technique for these types of images. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. Further the accuracy of Number Plate Extraction phase depends on the quality of captured vehicle image.
The overall accuracy and efficiency of whole ANPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase.
Mainly the ANPR system consists of 4 phases: - Acquisition of Vehicle Image and Pre-Processing, Extraction of Number Plate Area, Character Segmentation and Character Recognition. It is observed from the experiment that the developed system successfully detects and recognize the vehicle number plate on real images.Īutomatic Number Plate Recognition system is an application of computer vision and image processing technology that takes photograph of vehicles as input image and by extracting their number plate from whole vehicle image, it display the number plate information into text. The system is implemented and simulated in Matlab, and it performance is tested on real image. The resulting data is then used to compare with the records on a database so as to come up with the specific information like the vehiclepsilas owner, place of registration, address, etc. Optical character recognition technique is used for the character recognition. Vehicle number plate region is extracted using the image segmentation in an image. The developed system first detects the vehicle and then captures the vehicle image.
The system is implemented on the entrance for security control of a highly restricted area like military zones or area around top government offices e.g. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. Automatic number plate recognition (ANPR) is an image processing technology which uses number (license) plate to identify the vehicle.
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