Character recognition under Severe Perspective Distortion

Abstract

A common problem encountered in signboard recognition is the perspective distortion of characters. In this paper, we propose a method which is able to directly recognize characters under severe perspective distortion without perspective rectification. In this method, a character is represented by a sequence of cross ratio spectra, in which the perspective effect can be modeled as an one-dimensional uneven stretching. Dynamic Time Warping algorithm is employed to estimate the pairwise similarity between spectra of the query and spectra of a fronto-parallel template. Then, it is again used to find out the pixel-level correspondence and the similarity between the query and the template. The experiment results showed that the proposed method worked well on synthetic character images and signboards in real scene under severe perspective projections. © 2008 IEEE.