Abstract
Recently, local features especially point descriptors have received lots of interest in the computer vision and image processing communities. SIFT and SURF descriptors have shown their powerful usefulness on natural object recognition and classification. However, the use of local descriptors such as SIFT and SURF is still not very common in handwritten document image analysis now. In this paper, we propose an investigation on the description of handwriting by applying different interest points and local descriptors on historical handwritten images in the context of a coarse-to-fine segmentation-free word spotting method. The observation and analysis based on the experimental results can help optimizing the description of handwriting according to different applications.
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References
Andreas, F., Andreas, K., Volkmar, F., Horst, B.: HMM-Based Word Spotting in Handwritten Documents Using Subword Models. In: Proc. of the ICPR, pp. 3416–3419 (2010)
José, R., Florent, P.: Handwritten Word Spotting Using Hidden Markov Models and Universal Vocabularies. Pattern Recognition 42(9), 2106–2116 (2009)
Tony, M.R., Word Spotting, R.M.: for Historical Documents. In: Proc. of the 9th ICDAR, vol. 9(2-4), pp. 139–152 (2007)
Yann, L., Asma, O., Frank, L., Hubert, E.: Towards an Omnilingual Word Retrieval System for Ancient Manuscripts. Pattern Recognition 42(9), 2089–2105 (2009)
José, R.: Local Gradient Histogram Features for Word Spotting in Unconstrained Handwritten Documents. In: Proc. of the 11th ICFHR, pp. 7–12 (2008)
Marçal, R., David, A., Ricardo, T., Joseph, L.: Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method. In: Proc. of the 11th ICDAR, pp. 63–67 (2011)
Jean, C.: Utilisation des Points d’Intérêt pour Rechercher des Mots Imprimes ou Manuscrits dans les Documents Anciens. In: Proc. of CIFED, pp. 163–178 (2012)
Angelika, G., Robert, S., Markus, D.: Layout Analysis for Historical Manuscripts Using SIFT Features. In: Proc. of the 11th ICDAR, pp. 508–512 (2011)
Andrew, J.N., Lewis, D.G.: Multiscale Histogram of Oriented Gradient Descriptors for Robust Character Recognition. In: Proc. of the 11th ICDAR, pp. 1085–1089 (2011)
Peng, W.: Historical Document Image Analysis and Recognition. In: Proc. of the 5th JDT du LIRIS, pp. 35–36 (2013)
Youssouf, C., Robert, W., Mohamed, C.: TSV-LR: Topological Signature Vector-Based Lexicon Reduction for Fast Recognition of Pre-modern Arabic Subwords. In: Proc. of the 2011 Workshop on HIP, pp. 6–13 (2011)
Serge, B., Jitendra, M., Jan, P.: Shape Matching and Object Recognition Using Shape Contexts. IEEE trans. on PAMI 24, 509–522 (2002)
Richard, G.C., Eric, L.: Strategies in Character Segmentation: A Survey. In: Proc. of the 3rd ICDAR, pp. 1028–1033 (1995)
David, G.L.: Distinctive Image Features From Scale-Invariant Keypoints. Intl. Journal of Computer Vision 60(2), 91–110 (2004)
Herbert, B., Tinne, T., Luc, V.G.: Speeded-Up Robust Features(SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Cordelia, S., Roger, M., Christian, B.: Evaluation of Interest Point Detectors. Intl. Journal of Computer Vision 37(2), 151–172 (2000)
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Wang, P., Eglin, V., Largeron, C., McKenna, A., Garcia, C. (2013). Exploring Interest Points and Local Descriptors for Word Spotting Application on Historical Handwriting Images. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds) Computer Analysis of Images and Patterns. CAIP 2013. Lecture Notes in Computer Science, vol 8048. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40246-3_51
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DOI: https://doi.org/10.1007/978-3-642-40246-3_51
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