Character Recognition under Severe Perspective Distortion
Peng Zhou, Linlin Li, Chew Lim Tan · 2009
Perspective deformation is one of the main issues needed to be addressed in real-scene character recognition. An effective recognition approach, which is able to handle severe perspective deformation, is to employ cross ratio spectrum and dynamic time warping techniques. However, this solution suffers from a time complexity of O(n4). In this paper, a clustering based indexing method is proposed to index cross ratio spectra and thus expedite the recognition. Cross ratio spectra of all templates are clustered. A query is compared with the centroid of each cluster instead of spectra of all templates. Our method is 40 times faster than the previous method, and has archived about 15-time speed up while preserving almost the same recognition accuracy in the real scene character recognition experiment.