Automatic Number Plate Recognition System Rhythm Kr Dasgupta Country: India Email:
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Abstract-Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. There is a need to develop ANPR system along with the OCR most challenging application of as a one of the solutions to this problem because of the variable conditions encountered and the expected effectiveness. This literature describes the Smart Vehicle Screening System. A successful implementation depends also on complementary image processing techniques and tools. The Smart Vehicle Screening System, which can be installed into a tollbooth for automated recognition of vehicle license plate information using a photograph of a vehicle. In this literature, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Keywords-Automatic Number Plate Recognition (ANPR), Optical Character Recognition (OCR), Smart Vehicle Screening System. I. INTRODUCTION The Automatic number plate recognition (ANPR) is a block supervision approach that uses optical character recognition on images to view the license plates on vehicles. It is can be adjusted at number of public places for observation in some of the point like traffic refuge obligation, automatic toll text collection [1]. ANPR a technology that uses optical characters’ recognition on images to read vehicle registration plates. It can use existing closed-circuit television, road-rule enforcement cameras, or cameras specifically designed for the task. ANPR is used by police forces around the world for law enforcement purposes. It is also used for electronic toll collection on pay-per-use roads and as a method of cataloging the movements of traffic for example by highways agencies. Automatic number plate recognition can be used to store the images captured by the cameras as well as the text from the license plate, with some configurable to store a photograph of the driver. Systems commonly use infrared lighting to allow the camera to take the picture at any time of the day.[2],[3],[4] ANPR technology tends to be region-specific, owing to plate variation from place to place. Concerns about these
systems have centered on privacy fears of government tracking citizens' movements, misidentification, high error rates, and increased government spending. Automatic number plate recognition has two essential technological issues: 1. the quality of license plate recognition software with its applied recognition algorithms, and 2. the quality of image acquisition technology, the camera and the illumination [5]. It is also used for anodic toll collection on pay-per-use streets and as a process of recognizing the locomotion of traffic for example by highways departments. ANPR can be used to store the images captured by the cameras as well as the text from the license plate, with some configurable to store a photograph of the driver. Systems commonly use infrared lighting to allow the camera to take the picture at any time of the day. ANPR technology tends to be region-specific, owing to plate variation from place to place [6]. Most of the ANPR systems are based on common approaches like Probabilistic neural network (PNN) [5], Optical Character Recognition (OCR)[7],[8],[6],[9],[10], [11], Feature salient[12],MATLAB[13], Configurable method[14], BP neural network[15], color segmentation [16], color-discrete characteristics [17] etc. Some authors focus on improving resolution of the low-resolution image by using technique called super resolution [18], [19]. Sometimes it becomes necessary to assess the quality of ANPR system. Throughout this literature, number plate and license plate are used interchangeably. Development history ANPR was invented in 1976 at the Police Scientific Development Branch in the UK.] Prototype systems were working by 1979, and contracts were let to produce industrial systems, first at EMI Electronics, and then at Computer Recognition Systems (CRS) in Wokingham, UK. Early trial systems were deployed on the A1 road and at the Deptford Tunnel. However, it did not become widely used until new developments in cheaper and easier to use software was pioneered during the 1990s. The first arrest through detection of a stolen car was made in 1981and the first documented case of ANPR in helping solve a murder occurred in November 2005 after the murder of Sharon Beshenivsky, in which City of Bradford based ANPR played a vital role in locating and subsequently convicting her killers. [20] Technology The font on Dutch plates was changed to improve plate recognition. ANPR uses OCR on images taken by cameras. When Dutch vehicle registration plates switched to a different style in 2002, one of the changes made was to the font, introducing small gaps in some letters (such as P and R) to make them more distinct and therefore more legible to such systems. Some license plate arrangements use variations in font sizes and positioning—ANPR systems must be able to cope with such differences in order to be truly effective. More complicated systems can cope with international variants, though many programs are individually tailored to each country.
The cameras used can include existing road-rule enforcement or closed-circuit television cameras, as well as mobile units, which are usually attached to vehicles. Some systems use infrared cameras to take a clearer image of the plates.[21],[22],[23],[24],[25],[26],[27],[28],[29],[30]
IV. ALGORITHM a. Algorithms for the ANPR with OCR For a system of the ANPR with OCR it needs 7 algorithms process that the software requires for identifying a license plate. First one Plate localization – responsible for finding and isolating the plate on the picture. Second Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size. Third Normalization – adjusts the brightness and contrast of the image. Forth Character segmentation – finds the individual characters on the plates. After that Optical character recognition. The Syntactical/Geometrical analysis – check characters and positions against country-specific rules. At last the averaging of the recognized value over multiple fields/images to produce a more reliable or confident result. Especially since any single image may contain a reflected light flare, be partially obscured or other temporary effect. b. The OCR in the ANPR The ANPR with OCR works by using the technology to capture the image and retrieving the license numbers on the plate. It works by simply highlighting the numbers on the image and separating them from the other object on the screen. The ANPR with OCR will then work to convert the data into editable, searchable and easily stored information into the database network. Business entities can make great use of the ANPR with OCR in tracking and tracing destinations of the company owned vehicles. Law enforcement ANPR law has been imposed on certain countries like Australia, Belgium, Denmark, France, and Germany. VI. FUTURE WORK ANPR can be extra stunt for vehicle owner naming, vector dummy identification transit force, vector speed control and vector spot capturing. It can be extra continued as bilingual ANPR to analyze the articulation of appearances compulsorily placed on the buildup documents. It can turn out distinct betterments like traffic security execution, security- in case of green-eyed action by vehicle, smooth to use, instant notification opportunity- as contrast to seeking vehicle holder enrolling details manually and
profitable for any outland. For low boldness replicas some reformation methods like super constancy [31], [32] of figure should be centralize. Most of the ANPR attract on convert one vector number plate but in actual-time there can be more than one vector number plates while the images are being apprehended. In [11] different vector number plate images are express for ANPR during the maximum number of other setups offline images of vehicle, taken from online information bank such as [33] are disposed like input to ANPR so the abstract outcomes may depart from the outcomes display. To section different vector number plates a base- to-lovely approach [9] could be usable. VII. REFERENCES [1] You-Shyang Chen and Ching-Hsue Cheng, "A Delphi- based rough sets fusion model for extracting payment rules of vehicle license tax in the government sector," Expert Systems with Applications, vol. 37, no. 3, pp. 2161- 2174, 2010. [2]"ANPR Tutorial". ANPR Tutorial. 15 August 2006. Retrieved 2012-01-24.
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