Abstract
Twofold detection of the document images plays an important role in document image analysis. This paper presents a novel approach to detect twofold document images by extracting local features such as moment, texture and foreground pixel density. The performance of the proposed system is evaluated based on criteria of data schemes, feature and various distance metrics. Experimental results on different datasets demonstrates that proposed method is flexible enough to handle multilingual documents and provides better performance on historical, printed and handwritten documents. The performance of the proposed approach is analyzed with local features alone and better performance is observed when combined features are taken into account. Based on distance metric criteria, earth mover’s distance for similarity measurement outperforms the other distance measures.
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George, G., Sreeraj, M. (2016). Twofold Detection of Multilingual Documents Using Local Features. In: Thampi, S., Bandyopadhyay, S., Krishnan, S., Li, KC., Mosin, S., Ma, M. (eds) Advances in Signal Processing and Intelligent Recognition Systems. Advances in Intelligent Systems and Computing, vol 425. Springer, Cham. https://doi.org/10.1007/978-3-319-28658-7_45
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DOI: https://doi.org/10.1007/978-3-319-28658-7_45
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