Script identification using steerable Gabor filters

Multi-channel Gabor filtering has been widely used in texture classification. In this paper, Gabor filters have been applied to the problem of script identification in printed documents. Our work is divided into two stages. Firstly, a Gabor filter bank is appropriately designed so that extracted rotation-invariant features can handle scripts that are similar in shape and even share many characters. Secondly, the steerability property of Gabor filters is exploited to reduce the high computation cost resulted from the frequent image filtering, which is a common problem encountered in Gabor filter related applications. Results from preliminary experiments are quite promising, where Chinese, Japanese, Korean and English are considered. Over 98.5 % language identification rate can be achieved while image filtering operations have been reduced by 40%.