SITT - A Simple Robust Scaleinvariant Text Feature Detector

Marco Block, Marte Ramirez Ortegon, Alexander Seibert, Jan Kretzschmar, Raul Rojas— 2007

In this paper we present SITT, a simple robust scaleinvariant text feature detector for document mosaicing. Digital image stitching has been studied for several decades. SIFT-Features in combination with RANSAC algorithm are established to produce good panoramas. The main problem of realtime text document stitching is the size of the feature set created by SIFT-Features. We introduce SITT-Features to solve this problem. Our experiments denote that for document images SITT-Features produce faster good results than SIFT-Features.

TitelSITT - A Simple Robust Scaleinvariant Text Feature Detector
VerfasserMarco Block, Marte Ramirez Ortegon, Alexander Seibert, Jan Kretzschmar, Raul Rojas
VerlagFreie Universität Berlin, Institute of Computer Science
OrtTakustr. 9, 14195 Berlin, Germany
Datum200701
KennungB-07-02
Quelle/n
Spracheeng
ArtText