License plate recognition (LPR) technology is a mature yet imperfect technology used for automated toll collection and speed enforcement. The portion of license plates that can be correctly recognized and matched at two separate stations is typically in the range of 35% or less. Existing methods for improving the matching of plates recognized by LPR units rely on intensive manual data reduction, such that the misread plates are manually entered into the system. Recently, an advanced matching technique that combines Bayesian probability and Levenshtein text-mining techniques was proposed to increase the accuracy of automated license plate matching. The key component of this method is what we called the association matrix, which contains the conditional probabilities of observing one character at one station for a given observed character at another station. However, the estimation of the association matrix relies on the manually extracted ground truth of a large number of plates, which is a cumbersome and tedious process. To overcome this drawback, in this study, we propose an ingenious novel self-learning algorithm that eliminates the need for extracting ground truth manually. These automatically learned association matrices are found to perform well in the correctness in plate matching, in comparison with those generated from the painstaking manual method. Furthermore, these automatically learned association matrices outperform their manual counterparts in reducing false matching rates. The automatic self-learning method is also cheaper and easier to implement and continues to improve and correct itself over time.