page_title: Automatic number-plate recognition
section_title: Algorithms
hierarchical_section_title: Automatic number-plate recognition / Components / Algorithms
context_page_description: Automatic number-plate recognition is a technology that uses optical character recognition on images to read vehicle registration plates to create vehicle location data. 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, including to check if a vehicle is registered or licensed. It is also used for electronic toll collection on pay-per-use roads and as a method of cataloguing 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 day or night. ANPR technology must take into account plate variations from place to place.
  Privacy issues have caused concerns about ANPR, such as government tracking citizens' movements, misidentification, high error rates, and increased government spending. Critics have described it as a form of mass surveillance.
context_section_description: There are seven primary algorithms that the software requires for identifying a license plate:
  Plate localization – responsible for finding and isolating the plate on the picture
  Plate orientation and sizing – compensates for the skew of the plate and adjusts the dimensions to the required size
  Normalization – adjusts the brightness and contrast of the image
  Character segmentation – finds the individual characters on the plates
  Optical character recognition
  Syntactical/Geometrical analysis – check characters and positions against country-specific rules
  The averaging of the recognised 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.
  The complexity of each of these subsections of the program determines the accuracy of the system.  During the third phase (normalization), some systems use edge detection techniques to increase the picture difference between the letters and the plate backing. A median filter may also be used to reduce the visual noise on the image.
caption: Steps 2, 3 and 4: The license plate is normalized for brightness and contrast, and then the characters are segmented to be ready for OCR.
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