Review Paper: Licence Plate and Car Model Recognition
The main intention of this study paper is to explore and analyze the numerous methods that are used for license plate extraction and identification. Analysis is done by detailed study of the prevailing methodologies and their drawbacks were duly notified. Various concepts such as OCR reading, bounding method and other computational techniques were thoroughly studied and are recorded below. Thus, with the knowledge gained from this review, a pristine and a noble method were developed for the same by utilizing the concept of API. This not only involves extraction of characters from the license plate but also aids in the identification of the vehicle model, as a whole, with the help of image processing. This method is not merely effective for detection but also holds good for certain amount of automation with the aid of the upheld database. These techniques of detection backboned by databases can prove to be very effective in a wide range of use-cases in the current world. Such identification as well as compilation of useful data can be implemented in applications such as automatic toll system, fee collection in parking areas, traffic disciplinary maintenance, vehicle monitoring inside a premise and much more.
KeywordsLicense plate Vehicle identification Automatic toll system Automated parking fee collection API
License plate extraction and model identification [1, 2, 3, 4] of a vehicle has immense importance in various issues. Implementation of such system is mainly done in automatic toll collection , parking fee collection, etc. The earliest version of automobile identification was done with microwave and infrared system . Over the course of time, this process was further extended and using a CCD camera, image segmentation of license plate was processed . With advent of capturing technology, a method called Optical character recognition was devised to extract plate information under various circumstances . This includes places where an image of a vehicle in traffic has to be processed or in cases, where a vehicle has to be identified within a mixture of other objects. Texture analysis is applied along with statistical methods  to provide information that is obtained from traffic camera images. Usage of Tesseract engine and neural networks  has given more efficient character segmentation and identification. Based on prior knowledge on the images, an automated detection which detects only the edges of license plate was performed . This process helps to identify merely the license plate with the help of above-mentioned process. Reduction of noise can be avoided by the process of converting a colored image to a gray scale image. To allow recognition of each character, segmentation can be done by yet another method . In the same method, an image was converted to YDbDr format to specifically detect characters . The automated fee calculation was thus done using these recognition techniques, along with certain prescribed rules that was controlled via a program .
1.1 Various Techniques Involving Extraction of Characters from License Plate
Extraction of license plate characters has been extensively researched for various purposes. Some of the effective theories have been put forth in this section.
1.1.1 Optical Character Recognition [OCR]
1.1.2 Using Bounding Method
Results of the system 
Number of accuracy
Percentage of accuracy (%)
1.1.3 Extraction Process for Characters
The system suggested in paper  provides the process of masking the unwanted components obtained from the image through camera.
WA: width-threshold; HA: height-threshold; AA: area-thresholdmax
Modified Adaptive thresholding is utilized for segmentation process. This method proves its effectiveness in terms of lesser processing time and memory. However, it produces lesser computational power when compared to OCR.
1.1.4 Computation for Multiple License Plates
2 Suggested Method for License Plate Character Extraction
Thus, various methods were analyzed pertaining to license plate detection and recognition. Each method was studied thoroughly and were estimated and compared on their performance basis. By considering all the shortcomings in the subsisting systems, a novel method of image recognition was put forth for license plate detection along with car model recognition. The proposed method was tested in terms of performance, operating speed, cost and its effectiveness to be implemented in real-life situations. Under all these criteria, the proposed method of detection involving API provides precise output with a sturdy database to support in various applications. With the help of faster processing system by raspberry pi, computational error can also be minimized. Thus, by means of simpler programming structure, it can be made adaptable to various use-cases.
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