An Automatic & Robust Vehicle License/Number Plate Recognition System (Electronics Project)

ABSTRACT

An automatic and robust vehicle license plate recognition system has been developed. The proposed method uses scan line evaluation and averaging method to localize the number plate followed by a border removal mechanism combined with character mending and approximation of character height to extract the number plate characters.

Finally, a template matching approach is used to recognize the characters. A Graphical User Interface has been created and the algorithm is experimented successfully on a variety of real images, both single as well as double line plates. The sample results obtained on testing with various images are also detailed.

LITERATURE REVIEW

Plate Localization

Plate localization is done to remove the unwanted background details, and thereby focusing on to the essential details in the image. To detect the car plate, a method by applying a top-hat filter to the whole image followed by a multiscale region search has been described. Another approach has been proposed to detect the vertical edges, to extract the license plate using sobel operators.

Character Extraction

Character extraction is done by segmenting the character portions from the localized number plate. Noise contents that may also be present along with these number plate characters, makes the job a tedious one. A method has been proposed to remove the frame lines in the number plate followed by digit filtering. A method on color image segmentation, region growing and clustering methods are described.

Character Recognition

Character recognition is an essential and inevitable phase in such an application, where the system is put up to deal with nebulous or distorted characters in the license plate due to environmental hazards.

PROPOSED METHOD

Figure 1. Segmented region with three scan lines

As the image may have complex background details, number plate localization is the central issue that demands great attention. Scan line evaluation and averaging method is used here to accomplish this task. The image obtained from the sensor is filtered and binarized and the set of connected components are segmented. Following this step we measure the dimensions; height, width and area; of each of these segmented regions. Only rectangular segmented regions are taken into account.