Abstract:
Car plate detection is a key component in automatic
license plate recognition system. This paper adopts an
enhanced cascaded tree style learner framework for
car plate detection using the hybrid object features
including the simple statistical features and Harr-like
features. The statistical features are useful for
simplifying the process on cascade classifier. The
cascaded tree-style detector design will further reduce
the false alarm and the false dismissal while retaining
a high detection ratio. The experimental results
obtained by the proposed algorithm exhibit the
encouraging performance.
