Matching Vehicle License Plate Numbers Using License Plate Recognition and Text Mining Techniques

License plate recognition (LPR) technology has been widely applied in many different transportation applications such as enforcement, vehicle monitoring and access control. In most applications involving enforcement (e.g. cashless toll collection, congestion charging) and access control (e.g. car parking) a plate is recognized at one location (or checkpoint) and compared against a list of authorized vehicles. In this research I dealt with applications where a vehicle is detected at two locations and there is no list of reference for vehicle identification.

There seems to be very little effort in the past to exploit all information generated by LPR systems. In nowadays, LPR machines have the ability to recognize most characters on the vehicle plates even under the harshest practical conditions. Therefore, even though the equipment are not perfect in terms of plate reading, it is still possible to judge with certain confidence if a pair of imperfect readings, in the form of sequenced characters (strings), most likely belong to the same vehicle. The challenge here is to design a matching procedure in order to decide whether or not they belong to same vehicle.

In view of the aforementioned problem, this research intended to design and assess a matching procedure that takes advantage of a similarity measure called edit distance (ED) between two strings. The ED measure the minimum editing cost to convert a string to another. The study first attempted to assess a simple case of a dual LPR setup using the traditional ED formulation with 0 or 1 cost assignments (i.e. 0 if a pair-wise character is the same, and 1 otherwise). For this dual setup, this research has further proposed a symbol-based weight function using a probabilistic approach having as input parameters the conditional probability matrix of character association. As a result, this new formulation outperformed the original ED formulation. Lastly, the research sought to incorporate the passage time information into the procedure. With this, the performance of the matching procedure improved considerably resulting in a high positive matching rate and much lower (about 2%) false matching rate.