Authors:
Stefania Calarasanu
1
;
Séverine Dubuisson
2
and
Jonathan Fabrizio
1
Affiliations:
1
EPITA-LRDE, France
;
2
Sorbonne Universités, UPMC Univ Paris 06, CNRS, UMR 7222 and ISIR, France
Keyword(s):
Text Rectification, Text in Perspective.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Document Imaging in Business
;
Features Extraction
;
Image and Video Analysis
;
Shape Representation and Matching
Abstract:
A frequent challenge for many Text Understanding Systems is to tackle the variety of text characteristics in born-digital and natural scene images to which current OCRs are not well adapted. For example, texts in perspective are frequently present in real-word images, but despite the ability of some detectors to accurately localize such text objects, the recognition stage fails most of the time. Indeed, most OCRs are not designed to handle text strings in perspective but rather expect horizontal texts in a parallel-frontal plane to provide a correct transcription. In this paper, we propose a rectification procedure that can correct highly distorted texts, subject to rotation, shearing and perspective deformations. The method is based on an accurate estimation of the quadrangle bounding the deformed text in order to compute a homography to transform this quadrangle (and its content) into a horizontal rectangle. The rectification is validated on the dataset proposed during the ICDAR 2
015 Competition on Scene Text Rectification.
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