01-05-2017, 12:39 PM
This is a vehicle license plate / license recognition algorithm based on the very elementary technique of matching templates. The algorithm takes an input image of the number plate (the number plate must be dominant in the image) and after filtering the image, performs region-based operations. It then attempts to capture the regions of the characters in a processed binary image and with the help of the template that matches the output of the character string of the license plate.
The location of the license plate is an important part of the license plate recognition, it limits the search space of the recognition part, so it can be calculated more quickly. Enrollment recognition is an important issue in today's life. Today there are many motor vehicles on the road. It is useful to identify vehicles for many applications. For example, for automated tolling systems, traffic monitoring, public safety, speed control and road valuation.
Here is a need for intelligent traffic management systems to deal with constantly increasing traffic roads in the day. Video-based traffic monitoring is an important part of such systems. Information on current situations can be extracted automatically using image processing algorithms. In addition to vehicle detection and tracking, license plate identification is important for a wide variety of applications. These include, e.g. Access control, tracking of stolen vehicles or identification of dangerous drivers. Automatic number plate recognition systems are very popular and studied around the world. It has been classified into two main parts; Dishes are too far, camera low quality, motion blur, lack of lighting and low contrast due to excessive exposure, reflection or shadows, dirt on the plate. Image enhancement technique is very crucial based on filters to eliminate noise and unwanted effects of light in order to get clear and readable images are used.
The location of the license plate is an important part of the license plate recognition, it limits the search space of the recognition part, so it can be calculated more quickly. Enrollment recognition is an important issue in today's life. Today there are many motor vehicles on the road. It is useful to identify vehicles for many applications. For example, for automated tolling systems, traffic monitoring, public safety, speed control and road valuation.
Here is a need for intelligent traffic management systems to deal with constantly increasing traffic roads in the day. Video-based traffic monitoring is an important part of such systems. Information on current situations can be extracted automatically using image processing algorithms. In addition to vehicle detection and tracking, license plate identification is important for a wide variety of applications. These include, e.g. Access control, tracking of stolen vehicles or identification of dangerous drivers. Automatic number plate recognition systems are very popular and studied around the world. It has been classified into two main parts; Dishes are too far, camera low quality, motion blur, lack of lighting and low contrast due to excessive exposure, reflection or shadows, dirt on the plate. Image enhancement technique is very crucial based on filters to eliminate noise and unwanted effects of light in order to get clear and readable images are used.
It can be understood in the following video: