TheAuto Recognition of License Plate systemexist for anextended time, however just in the late 90s, it transformed into a basic application due to the substantial addition in the aggregate of vehicles.The data removed from the tags is for the most part utilized for activity observing, get to control, stopping, motorway street tolling, and making auto logs for stopping frameworks, travel time estimation and so forward by the law authorization organizations. The recognition issue is by and large subdivided into 5 sections: (1) image acquisition i.e. capturing the picture of the number plate.(2) pre-processing the image, i.e. standardization, altering the illumination, skewness and difference of the image (3) character segmentation i.e. finding and distinguishing the individual image on the plate, (4) optical character recognition. There might be further refinements over these (like coordinating the vehicle permit number with a specific database to track speculated vehicles, and so on.) however the major structure continues as before. A controlling parameter in such manner is nation particular movement standards and gauges. This fines tune the framework, i.e. amount of characters in the tag, content luminance level (relative file, i.e. dim content on light foundation or light content on dull foundation) and so forward. So the issue can then be limited for application in a specific nation. For instance, in India the standard is printing the tag numbers in dark shading on a white foundation for private vehicles and on a yellow foundation for business vehicles. The general configuration of the tag is two letters (for state code) trailed by locale code, then a four digit code particular to a specific vehicle. In U.S.A no strict rules have been set with respect to the textual styles that can be exploited for this reason. The vehicle number plate is an example with high varieties of contrast[1]. This paper gives a summary of the research work so far around there and the methods utilized in built up a LPR system .The work is isolated into a few sections:image acquisition,number plate extraction, and number plate Character separation and number plate recognition stages [2]. Image Acquisition: This stage manages getting a image by a acqusition technique. In our proposed system, we utilized a high or low determination computerized camera and sufficient light source to secure the information picture. Number Plate Extraction: This stage removes the locale of intrigue, i.e., the tag, from the gained picture. The proposed approach includes “Covering of a district with high or low likelihood of tag and afterward examining the entire veiled area for tag.