The variables and the variation scope considered in each variable would be different for different applications of vehicle license plate recognition (VLPR). This research splits major VLPR applications into three categories: access control, traffic law enforcement, and road patrol. Each category is characterized by the variables, including plate size and illumination condition, camera viewpoint, with different scopes of variation. Applications with more variables or larger variation scopes, as in road patrol cases, require more sophisticated methods and higher computational cost than those with fewer variables or less variation scopes, as in the access control cases. It is uneconomic to apply the methods developed for road patrol to handle access control. On the contrary, a method developed for access control cannot solve most cases in road patrol. Different from most previous works without specifying applications, this paper redefines the VLPR problem using the variables and their variation scopes for the three major applications. Because no benchmark database is available for the evaluation of VLPR methods on the three major applications, a database composed of three application-oriented subsets is introduced and made available to the research community. There has not been a method commonly acknowledged as a baseline although VLPR has been an active research topic for more than a decade. A modular baseline method, whose components can be adjusted for the three applications, is proposed and benchmarked on our database. The proposed baseline is compared against a few competitive ones to highlight its value as a benchmark.