A Novel Approach to Extract Text From License Plate of Vehicles

Abstract

In this paper the text found on the vehicle plates is detected from the input image and this requires the localization of number plate area in order to identify the characters present on it. In this work, simple color conversion edge detection and connector measurement technique with the application of median filter as one of the operators is attempted. This paper presents an approach using simple but efficient morphological operations, filtering and finding connected components for localization of Indian number plates. The algorithm has been tested on 100 samples and is found to extract both alphabets and numbers from vehicle license plates images with an accuracy of 93% for both two and four wheeler license plates of Chennai region.

Classification and Comparison of License Plates Localization Algorithms

“…In [13] the extraction of the image contours is performed by the second derivative via the Laplacian Of Gaussian (LOG) filter followed by a dilation to refine the contours and connect objects, and by classifying candidates to "text" and "no text", the LP is exactly located. These algorithms showed a great improvement of the localization rate.…”

The Review on Automatic License Plate Recognition (Alpr)

“…Approach explained in [13] that, (1) detect a license plate region by vertical or a horizontal edge based method (2) preprocessing: is also needed in this approach .so first converted in to binary image then eliminate noise using morphological operation (3) character segmentation by thresholding method (4) feature extraction and character recognition by Euler number formation .Advantage of this approach that skew ness is not present in the detected vehicle number plate compare to other methods and Disadvantage is it limits the efficiency of the total system. By [14] (1)plate extraction by Mathematical Morphology approach and then ,Dilation and Erosion is apply to image,(2)segmentation by Structuring Elements approach, Meredian Filter technique and Edge Detection Methods are use,(3)character extraction by Pre-processing, Text/non-text classification approach .result derived by this paper is as given, Real Time Data:100,Images correctly Detected:93,Results Accuracy 93%.and it says that Very much damaged plate cannot be recognize. As per literature study all over system efficiency per month/year as given by chart figure9 which is vary according to methods for different steps for reconditioning of license plate, by the given plot we can observe percentage efficiency of system as per given data.…”

A License Plate Recognition System with Robustness against Adverse Environmental Conditions Using Hopfield’s Neural Network

“…On the one hand, the effects of external factors such as sunlight and car headlights, license plates with inappropriate designs, the wide variety of license plates and, on the other hand, the limited quality of the software and hardware related to the camera, have reduced the accuracy of these systems. However, recent advances in software and hardware have made LPR systems much safer and more widespread [3,4]. A countless number of these systems are working around the world and are growing exponentially and can do more tasks automatically in different market segments.…”