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2018 13th IAPR International Workshop on Document Analysis Systems (DAS) (2018)
Vienna, Austria
April 24, 2018 to April 27, 2018
ISBN: 978-1-5386-3346-5
pp: 79-84
ABSTRACT
Reading the text embedded in natural scene images is essential to many applications. In this paper, we propose a method for detecting text in scene images based on multi-level connected component (CC) analysis and learning text component features via convolutional neural networks (CNN), followed by a graph-based grouping of overlapping text boxes. The multi-level CC analysis allows the extraction of redundant text and non-text components at multiple binarization levels to minimize the loss of any potential text candidates. The features of the resulting raw text/non-text components of different granularity levels are learned via a CNN. Those two modules eliminate the need for complex ad-hoc preprocessing steps for finding initial candidates, and the need for hand-designed features to classify such candidates into text or non-text. The components classified as text at different granularity levels, are grouped in a graph based on the overlap of their extended bounding boxes, then, the connected graph components are retained. This eliminates redundant text components and forms words or textlines. When evaluated on the "Robust Reading Competition" dataset for natural scene images, our method achieved better detection results compared to state-of-the-art methods. In addition to its efficacy, our method can be easily adapted to detect multi-oriented or multi-lingual text as it operates at low level initial components, and it does not require such components to be characters.
INDEX TERMS
document image processing, feature extraction, graph theory, image classification, image segmentation, natural scenes, neural nets, text analysis, text detection
CITATION

W. Khlif, N. Nayef, J. Burie, J. Ogier and A. Alimi, "Learning Text Component Features via Convolutional Neural Networks for Scene Text Detection," 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), Vienna, Austria, 2018, pp. 79-84.
doi:10.1109/DAS.2018.65
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