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Text localization using standard deviation analysis of structure elements and support vector machines

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Author(s): Zagoris Konstantinos | Chatzichristofis Savvas | Papamarkos Nikos

Journal: EURASIP Journal on Advances in Signal Processing
ISSN 1687-6172

Volume: 2011;
Issue: 1;
Start page: 47;
Date: 2011;
Original page

ABSTRACT
Abstract A text localization technique is required to successfully exploit document images such as technical articles and letters. The proposed method detects and extracts text areas from document images. Initially a connected components analysis technique detects blocks of foreground objects. Then, a descriptor that consists of a set of suitable document structure elements is extracted from the blocks. This is achieved by incorporating an algorithm called Standard Deviation Analysis of Structure Elements (SDASE) which maximizes the separability between the blocks. Another feature of the SDASE is that its length adapts according to the requirements of the application. Finally, the descriptor of each block is used as input to a trained support vector machines that classify the block as text or not. The proposed technique is also capable of adjusting to the text structure of the documents. Experimental results on benchmarking databases demonstrate the effectiveness of the proposed method.
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