Skew Estimation by Instances
Seiichi Uchida, Megumi Sakai, Masakazu Iwamura, Shinichiro Omachi, Koichi Kise · 2008
This paper proposes a novel skew estimation method by instances. The instances to be learned (i.e., stored) are rotation invariants and a rotation variant for each character category. Using the instances, it is possible to estimate a skew angle of each individual character on a document. This fact implies that the proposed method can estimate the skew angle of a document where characters do not form long straight text lines. Thus, the proposed method will be applicable to various documents such as signboard images captured by a camera. Experimental evaluation using synthetic and real images revealed the expected robustness against various character layouts.