The main focus of this research project is to experiment deeply with and find alternative solutions to the image segmentation and character recognition problems within the License Plate Recognition framework. Three main stages are identified in such applications.
First, it is necessary to locate and extract the license plate region from a larger scene image.
Second, having a license plate region to work with, the alphanumeric characters in the plate need to be extracted from the background.
Third, deliver them to an OCR system for recognition. In order to identify a vehicle by reading its license plate successfully, it is obviously necessary to locate the plate in the scene image provided by some acquisition system (e.g. video or still camera).
Classification with different blob color for representation
Vehicle Counting with Classification
Counting output will be shown on screen
Basic GUI to select file and video preview of detection and counting
Choosing category for counting
GUI enabling selection of points to enable drawing line
Timestamp of vehicle counting
We have designed and developed high-quality recognition software for the automatic recognition of vehicle license plates. We market our number plate recognition software as a number plate recognition engine along with a software development kit (SDK).
License Plate Detection: This is the first and probably the most important stage of the system. It is at this stage that the position of the license plate is determined. The input at this stage is an image of the vehicle and the output is the license plate.
Character Segmentation: It’s at this stage the characters on the license plate are mapped out and segmented into individual images.
Character Recognition: This is where we wrap things up. The characters earlier segmented are identified here. We’ll be using machine learning for this.