Abstract
Spotting is finding the location of a particular object without explicitly knowing the entire content in a collection of objects. In this chapter, we consider two types of objects. We consider the word in a document image as an object. Another object is an artifact that is present in terracotta panel images. The proposed object spotting method is based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query image and the document/panel image are smoothened first, and then the Scale Invariant Feature Transform detector is used to obtain the keypoints in both the query image and the document/panel image. Each keypoint is described in terms of WKS. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. In the case of word spotting, a two-step searching technique is introduced to find the region of interest in the document image under test. On the other hand, a single-step searching technique is used to spot figures present in the panel image corresponding to a particular query image. The proposed method is tested on three historical Bangla handwritten datasets and one historical English handwritten dataset, as well as a terracotta panel image dataset. The performance of the proposed method is evaluated using standard metrics.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Albanese, M., d’Acierno, A., Moscato, V., Persia, F., Picariello, A.: A multimedia semantic recommender system for cultural heritage applications. In: Proc. of 5th International Conference on Semantic Computing (ICSC), pp. 403–410. IEEE, Piscataway (2011)
Aletras, N., Stevenson, M., Clough, P.: Computing similarity between items in a digital library of cultural heritage. J. Comput. Cult. Herit. 5(16), 1–19 (2012)
Almazán, J., Gordo, A., Fornés, A., Valvenya, E.: Segmentation-free word spotting with exemplar SVMs. Pattern Recognit. 47, 3967–3978 (2014)
Amato, G., Falchi, F., Gennaro, C.: Fast image classification for monument recognition. J. Comput. Cult. Herit. 8(18), 1–25 (2015)
Ardizzone, E., Chella, A., Pirrone, R., Gambino, O.: An image retrieval system for artistic database on cultural heritage. In: Proc. della Conferenza Italiana sui Sistemi Intelligenti (CISI), pp. 1–8. Citeseer (2004)
Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: a quantum mechanical approach to shape analysis. In: Proc. of International conference on Computer Vision, Workshop, pp. 1626–1623. IEEE, Piscataway (2011)
Bacci, M., Bianchi, G., Campana, S., Fichera, G.: Historical and archaeological analysis of the church of the nativity. J. Cult. Herit. 13(4), e5–e26 (2012)
Bay, H., Ess, A., Tuytelaars, T., van Gool, L.: Speeded-up robust features (SURF). Comput. Vis. Image Underst. 110(3), 346–359 (2008)
BHID: Bishnupur Heritage Image Database. http://www.isical.ac.in/~bsnpr. Accessed 7 Mar 2017
Chen, G.-F.: Intangible cultural heritage preservation: an exploratory study of digitization of the historical literature of Chinese Kunqu Opera Librettos. J. Comput. Cult. Herit. 7(4), 1–16 (2014)
Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: Proc. of Workshop on Statistical Learning in Computer Vision, European Conference on Computer Vision, pp. 1–22 (2004)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proc. of Computer Vision and Pattern Recognition, pp. 886–893. IEEE, Piscataway (2005)
Doubek, P., Matas, J., Perdoch, M., Chum, O.: Image matching and retrieval by repetitive patterns. In: Proc. of 20th International Conference on Pattern Recognition (ICPR), pp. 3195–3198. IEEE, Piscataway (2010)
Fischer, A., Keller, A., Frinken, V., Bunke, H.: HMM-based word spotting in handwritten documents using subword models. In: Proc. of 20th International Conference on Pattern Recognition (ICPR), pp. 3416-3419. IEEE, Piscataway (2010)
Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character HMMs. Pattern Recognit. Lett. 33(7), 934–942 (2012)
Frinken, V., Fischer, A., Manmatha, R., Bunke, H.: A novel word spotting method based on recurrent neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 34, 211–224 (2012)
Hast, A., Fornés, A.: A segmentation-free handwritten word spotting approach by relaxed feature matching. In: Proc. of 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 150–155. IEEE, Piscataway (2016)
Iakovidis, D., Kotsifakos, E.E., Pelekis, N., Karanikas, H., Kopanakis, I., Mavroudakis, T., Theodoridis, Y.: Pattern-based retrieval of cultural heritage images. In: Proc. of the 11th Panhellenic Conference in Informatics (PCI) (2007)
Kesidis, A.L., Galiotou, E., Gatos, B., Pratikakis, I.: A word spotting framework for historical machine-printed documents. Int. J. Doc. Anal. Recognit. 14, 131–144 (2011)
Khurshid, K., Faure, C., Vincen, N.: Word spotting in historical printed documents using shape and sequence comparisons. Pattern Recognit. 45, 2598–2609 (2012)
Kolomenkin, M., Leifman, G., Shimshoni, I., Tal, A.: Reconstruction of relief objects from archeological line drawings. J. Comput. Cult. Herit. 6(3), 1–19 (2013)
Konidaris, T., Kesidis, A.L., Gatos, B.: A segmentation-free word spotting method for historical printed documents. Pattern Anal. Appl. 19(4), 963–976 (2016)
Kushki, A., Androutsos, P., Plataniotis, K.N., Venetsanopoulos, A.N.: Retrieval of images from artistic repositories using a decision fusion framework. IEEE Trans. Image Process. 13(3), 277–292 (2004)
Lavrenko, V., Rath, T., Manmatha, R.: Holistic word recognition for handwritten historical documents. In: Proc. of First International Workshop in Document Image Analysis for Libraries, pp. 278–287. IEEE, Piscataway (2004)
Lee, D.R., Hong, W., Oh, I.S.: Segmentation-free word spotting using SIFT. In: Proc. of Southwest Symposium on Image Analysis and Interpretation, pp. 65–68. IEEE, Piscataway (2012)
Lewis, P.H., Martinez, K., Abas, F.S., Fauzi, M.F.A., Chan, S.C.Y., Addis, M.J., Boniface, M.J., Grimwood, P., Stevenson, A., Lahanier, C., Stevenson, J.: An integrated content and metadata based retrieval system for art. IEEE Trans. Image Process. 13(3), 302–313 (2004)
Leydier, Y., Ouji, A., LeBourgeois, F., Emptoz, H.: Towards an omnilingual word retrieval system for ancient manuscripts. Pattern Recognit. 42, 2089–2105 (2009)
Liang, Y., Fairhurst, M.C., Guest, R.M.: A synthesised word approach to word retrieval in handwritten documents. Pattern Recognit. 45(12), 4225–4236 (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 90–110 (2004)
Mallik, A., Chaudhury, S., H Ghosh. Nrityakosha: preserving the intangible heritage of Indian classical dance. J. Comput. Cult. Herit. 4(11), 1–25 (2011)
Manmatha, R., Han, C., Riseman, E.: Word spotting: a new approach to indexing handwriting. In: Proceedings of IEEE Computer Vision and Pattern Recognition, pp. 631–637 (1996)
Marti, U.V., Bunke, H.: Using a statistical language model to improve the performance of an HMM-based cursive handwriting recognition systems. Int. J. Pattern Recognit. Artif. Intell. 15, 65–90 (2001)
Meyer, M., Desbrun, M., Schröder, P., Bar, A.H.: Discrete differential geometry operators for triangulated 2-manifolds. In: Proc. of Visualization Mathematics. Springer, Berlin, pp. 35–57 (2002)
Mishra, S., Mukherjee, J., Mondal, P., Aswatha, S.M., Mukherjee, J.: Real-time retrieval system for heritage images. In: Proc. of Emerging Research in Electronics, Computer Science and Technology, pp. 245–253. Springer, Berlin (2014)
Moreno-Noguer, F.: Deformation and illumination invariant feature point descriptor. In: Proc. of Computer Vision and Pattern Recognition (CVPR), pp. 1593–1600. IEEE, Piscataway (2011)
Nagy, G.: Twenty years of document image analysis in PAMI. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 38–62 (2000)
Panda, J., Sharma, S., Jawahar, C.V.: Heritage App: annotating images on mobile phones. In: Proc. of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), number 3. ACM, New York (2012)
Picard, D., Gosselin, P.H., Gaspard, M.C.: Challenges in content-based image indexing of cultural heritage collections. IEEE Signal Processing Mag. 32(4), 95–102 (2015)
Pinkall, U., Polthier, K.: Computing discrete minimal surfaces and their conjugates. Exp. Math. 2, 15–36 (1993)
Polpinij, J., Sibunruang, C.: Thai heritage images classification by Naive Bayes image classifier. In: Proc. of 6th International Conference on Digital Content, Multimedia Technology and Its Applications (IDC), pp. 221–224. IEEE, Piscataway (2010)
Rath, T.M., Manmatha, R.: Word image matching using dynamic time warping. In: Proc. of Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 521–527. IEEE, Piscataway (2003)
Rath, T., Manmatha, R.: Word spotting for historical documents. Int. J. Doc. Anal. Recognit. 9, 139–152 (2007)
Rodríguez, J., Perronnin, F.: Local gradient histogram features for word spotting in unconstrained handwritten documents. In: Proc. of International Conference on Frontiers in Handwriting Recognition (ICFHR) (2008)
Rodriguez-Serrano, J., Perronnin, F.: A Model-based sequence similarity with application to handwritten word spotting. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2108–2120 (2012)
Rothacker, L., Fink, G.A., Banerjee, P., Bhattacharya, U., Chaudhuri, B.B.: Bag-of-features HMMs for segmentation-free Bangla word spotting. In: Proc. of the 4th International Workshop on Multilingual OCR, vol. 5. ACM, New York (2013)
Rusiñol, M., Aldavert, D., Toledo, R., Lladós, J.: browsing heterogeneous document collections by a segmentation-free word spotting method. In: Proc. of International Conference on Document Analysis and Recognition (ICDAR), vol. 22, pp. 63–67. IEEE, Piscataway (2011)
Rusiñol, M., Aldavert, D., Toledo, R., Lladós, J.: Efficient segmentation-free keyword spotting in historical document collections. Pattern Recognit. 48(2), 545–555 (2015)
Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multiscale signature based on heat diffusion. Comput. Graph. Forum 28, 1383–1392 (2009)
Syeda-Mahmood, T.: Indexing of handwritten document images. In: Proc. of Workshop on Document Image Analysis, pp. 66–73. IEEE, Piscataway (1997)
Teraswa, K., Tanake, Y.: Slit style HOG feature for document image word spotting. In: Proc. International Conference of Document Analysis and Recognition (ICDAR), pp. 116–120. IEEE, Piscataway (2009)
The Asiatic Society, Kolkata. https://asiaticsocietycal.com
Trier, I.D., Jain, A.K., Taxt, T.: Feature extraction methods for character recognition—a survey. Pattern Recognit. 29(4), 641–662 (1996)
Vecco, M.: A definition of cultural heritage: from the tangible to the intangible. J. Cult. Herit. 11(3), 321–324 (2010)
Vrochidis, S., Doulaverakis, C., Gounaris, A., Nidelkou, E., Makris, L., Kompatsiaris, I.: A hybrid ontology and visual-based retrieval model for cultural heritage multimedia collections. Int. J. Metadata Semant. Ontol. 3(3), 167–182 (2008)
Zagoris, K., Pratikakis, I., Gatos, B.: Segmentation-based historical handwritten word spotting using document-specific local features. In: Proc. of International Conference on Frontiers in Handwritten Recognition (ICFHR), pp. 9–14 (2014)
Zhang, X., Tan, C.L.: Segmentation-free keyword spotting for handwritten documents based on heat kernel signature. In: Proc. of International Conference of Document Analysis and Recognition (ICDAR), pp. 827–831. IEEE, Piscataway (2013)
Zhang, X., Pal, U., Tan, C.L.: Segmentation-free keyword spotting for Bangla handwritten documents. In: Proc. of International Conference on Frontiers in Handwritten Recognition (ICFHR), pp. 381–386 (2014)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Das, S., Mandal, S. (2021). Object Spotting in Historical Documents. In: Mukhopadhyay, J., Sreedevi, I., Chanda, B., Chaudhury, S., Namboodiri, V.P. (eds) Digital Techniques for Heritage Presentation and Preservation. Springer, Cham. https://doi.org/10.1007/978-3-030-57907-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-57907-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57906-7
Online ISBN: 978-3-030-57907-4
eBook Packages: Computer ScienceComputer Science (R0)