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Author Risnumawan, Anhar ♦ Shivakumara, Palaiahankote ♦ Chan, Chee Seng ♦ Tan, Chew Lim
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Robust Arbitrary Text Detection System ♦ Natural Scene ♦ Text Pixel Candidate ♦ Curved Text Evaluation ♦ Mutual Direction Symmetry ♦ Text Representative ♦ Natural Scene Image ♦ Spatial Study ♦ Great Variety ♦ Computer Vision Problem ♦ Non-text Component ♦ Benchmark Datasets ♦ Pixel Distribution ♦ Non-horizontal Straight Text Evaluation ♦ Neighbour Criterion ♦ Text Direction ♦ Mutual Magnitude Symmetry ♦ Real World Image ♦ Gradient Vector Symmetry ♦ Sift Feature ♦ Character Orientation ♦ Unconstrained Environment ♦ Text Component ♦ Horizontal Text Evaluation ♦ Similar Behaviour ♦ Robust System
Abstract Text detection in the real world images captured in unconstrained environment is an important yet challenging computer vision problem due to a great variety of appearances, cluttered background, and character orientations. In this paper, we present a robust system based on the concepts of Mutual Direction Symmetry (MDS), Mutual Magnitude Symmetry (MMS) and Gradient Vector Symmetry (GVS) properties to identify text pixel candidates regardless of any orientations including curves (e.g. circles, arc shaped) from natural scene images. The method works based on the fact that the text patterns in both Sobel and Canny edge maps of the input images exhibit a similar behaviour. For each text pixel candidate, the method proposes to explore SIFT features to refine the text pixel candidates, which results in text representatives. Next an ellipse growing process is introduced based on a nearest neighbour criterion to extract the text components. The text is verified and restored based on text direction and spatial study of pixel distribution of components to filter out non-text components. The proposed method is evaluated on three benchmark datasets, namely, ICDAR2005 and ICDAR2011 for horizontal text evaluation, MSRA-TD500 for non-horizontal straight text evaluation and on our own dataset (CUTE80) that consists of 80 images for curved text evaluation to show its effectiveness and superiority over existing methods.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study