Title :
Skew Estimation by Instances
Author :
Uchida, Seiichi ; Sakai, Megumi ; Iwamura, Masakazu ; Omachi, Shinichiro ; Kise, Koichi
Abstract :
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.
Keywords :
Context modeling; Data mining; Graphics; Image recognition; Information analysis; Information retrieval; Robust stability; Shape; Text analysis; Vocabulary; document image analysis; instance-based learning; invariant; skew estimation; variant;
Conference_Titel :
Document Analysis Systems, 2008. DAS '08. The Eighth IAPR International Workshop on
Conference_Location :
Nara, Japan
Print_ISBN :
978-0-7695-3337-7
DOI :
10.1109/DAS.2008.22