ALGORITHMIC AND MATHEMATICAL
PRINCIPLES OF AUTOMATIC NUMBER PLATE
It is believed that there are currently more than half a billion cars on the roads worldwide. All those vehicles have their vehicle identification number ("VIN") which states a legal license to participate in the public traffic.
No vehicle without properly mounted, well visible and well readable license plate should run on the roads.
The license number is the most important identification data a computer system should treat when dealing with vehicles.
Suppose a company's security manager would like to have a system that precisely tells at every moment where the cars of the company are: in the garage or out on roads. The key issue of this task is that the registration of the movement of the vehicles should be done automatically by the system, otherwise it would require manpower.
Strictly speaking License Plate Recognition System is an integrated hardware + software device that reads the vehicles license plate and outputs the license plate number in ASCII - to some data processing system. Again, License Plate Recognition means Automated Data Input where Data equals the registration number of the vehicle.
License Plate Recognition Algorithms and Technology
Automatic license plate recognition has two essential technological issues: · the quality of the license plate recognition software with its applied recognition algorithms, and · the quality of the optical technology.
The highest the quality of the recognition software is:
· the highest recognition accuracy it has,
· the fastest processing speed it has,
· the most type of plates it can handle,
· the widest range of picture quality it can handle,
· the most tolerant against distortions of input data it is.
In early years of LPR available software were bound to specific countries. One software could read - for example - Spanish plates only, other could read plates from Hong Kong only, etc. This was not accidental: the geometrical structure of the plate as well as its syntax were essential parts of the plate reader software. Without the presumption of a fixed plate geometry (character ratios, character distribution, font type, plate colour, etc.) and a well defined syntax the algorithm may not even found the plate on the picture.
A good algorithm should read all plates from Europe with the same level of quality.
Needless to say that the better the quality of the input images are, the better conditions the license plate recognition algorithm has, and thus the higher license plate recognition accuracy can be expected to be achieved.
What does good image quality mean?
In order to expect reasonable results from a plate recognition algorithm, the processed images should contain a plate · with reasonable good spatial resolution,
· with reasonable good sharpness,
· with reasonable high contrast,
· under reasonable good lighting conditions,
· in a reasonable good position and angle of view.
Indeed, 'reasonable' is not an exact definition, still it has a well understandable meaning. Here are some problematic images:
Low spatial resolution (too small characters on the plate)
Bad lighting conditions (shadow and strong light)
An image acquisition system is considered to be good if it provides a stable, balanced, reasonable good image quality under all of its working conditions. If an LPR system has to work outdoor 24h/day, 7days/week in Middle-Europe, than it has to handle quite a wide range of lighting and weather conditions.
License Plate Recognition Software
OCR - suggest a computer program, a software, a set of algorithms much rather than some hardware devices or integrated systems to be understood under the term 'License Plate Recognition'.
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