Authors:
Muhammad Ali
and
Hassan Foroosh
Affiliation:
University of Central Florida, United States
Keyword(s):
Natural Scene Text Recognition, Active Contours, Holistic Character Recognition.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Shape Representation and Matching
Abstract:
Local features like Histogram of Gradients (HoG), Shape Contexts (SC) etc. are normally used by research community concerned with text recognition in natural scene images. The main issue that comes with this approach is ad hoc rasterization of feature vector which can disturb global structural and spatial correlations while constructing feature vector. Moreover, such approaches, in general, don’t take into account rotational invariance property that often leads to failed recognition in cases where characters occur in rotated positions in scene images. To address local feature dependency and rotation problems, we propose a novel holistic feature based on active contour model, aka snakes. Our feature vector is based on two variables, direction and distance, cumulatively traversed by each point as the initial circular contour evolves under the force field induced by the image. The initial contour design in conjunction with cross-correlation based similarity metric enables us to account
for rotational variance in the character image. We use various datasets, including synthetic and natural scene character datasets, like Chars74K-Font, Chars74K-Image, and ICDAR2003 to compare results of our approach with several baseline methods and show better performance than methods based on local features (e.g. HoG). Our leave-random-one-out-cross validation yields even better recognition performance, justifying our approach of using holistic character recognition.
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