Abstract
In this paper, we present a segmentation-free word spotting method based on Wave Kernel Signature (WKS) under the foundation of quantum mechanics. The query word and the document page are smoothened first, then SIFT detector is used to obtain the keypoints in both the query image and the document page. A window is placed centered at each keypoint to obtain the WKS descriptors. The WKS descriptors represent the average probability of measuring a quantum mechanical particle at a specific location based on quantum energy. We use an efficient search technique which calculates minimum energy difference between query word and document image to spot where the query word appears in the document image. The proposed method is tested on three historical Bangla handwritten datasets, one Bangla handwritten dataset, one old Bangla-printed dataset and one historical English handwritten dataset. To substantiate the goodness of the proposed method, its performance is measured using standard metrics.
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References
Almaza’n J, Gordo A, Forne’s A, Valvenya E (2014) Segmentation-free word spotting with exemplar svms. Pattern Recognit 47:3967–3978
Aubry M, Schlickewei U, Cremers D (2011) Pose-consistent 3D shape segmentation based on a quantum mechanical feature descriptor. pp. 122–131. Lecture Notes in Computer Science, Springer
Aubry M, Schlickewei U, Cremers D (2011) The wave kernel signature: A quantum mechanical approach to shape analysis. In: Proceedings of international conference on computer vision, Workshop, IEEE, pp. 1626–1623
Bag S, Harit G (2013) A survey on optical character recognition for Bangla and Devanagari scripts. Sadhana 38(1):133–168
Bay H, Ess A, Tuytelaars T, van Gool L (2008) Speeded-up robust features (surf). Comput Vis Image Underst (CVIU) 110(3):346–359
Chaudhuri BB, Pal U (1998) A complete printed Bangla OCR system. Pattern Recognit 31(5):531–549
Chris H, Mike S (1988) A combined corner and edge detector. In: Alvey vision conference, pp. 147–151
Csurka G, Dance CR, Fan L, Willamowski J, Bray C (2004) Visual categorization with bags of keypoints. In: Proceedings of workshop on statistical learning in computer vision, European conference on computer vision, pp. 1–22
Fischer A, Keller A, Frinken V, Bunke H (2010) Hmm-based word spotting in handwritten documents using subword models. In: Proceedings of international conference on pattern recognition, IEEE, pp. 3416–3419
Fischer A, Keller A, Frinken V, Bunke H (2012) Lexicon-free handwritten word spotting using character hmms. Pattern Recognit Lett 33(7):934–942
Frinken V, Fischer A, Manmatha R, Bunke H (2012) A novel word spotting method based on recurrent neural networks. IEEE Trans Pattern Anal Mach Intell 34:211–224
Hast A, Fornés A (2016) A segmentation-free handwritten word spotting approach by relaxed feature matching. In: 2016 12th IAPR workshop on document analysis systems (DAS), IEEE, pp. 150–155
Howe NR (2013) Part-structured inkball models for one-shot handwritten word spotting. In: Proceedings of international conference on document analysis and recognition (ICDAR), pp. 582–586
Kesidis AL, Galiotou E, Gatos B, Pratikakis I (2011) A word spotting framework for historical machine-printed documents. Int J Doc Anal Recognit IJDAR 14:131–144
Khurshid K, Faure C, Vincen N (2012) Word spotting in historical printed documents using shape and sequence comparisons. Pattern Recognit 45:2598–2609
Konidaris T, Kesidis AL, Gatos B (2016) A segmentation-free word spotting method for historical printed documents. Pattern Anal Appl 19(4):963–976
Lavrenko V, Rath T, Manmatha R (2004) Holistic word recognition for handwritten historical documents. In: Proceedings of document image analysis for libraries, first international workshop, IEEE, pp. 278–287
Lee DR, Hong W, Oh IS (2012) Segmentation-free word spotting using sift. In: Proceedings of Southwest Symposium on Image Analysis and Interpretation, IEEE, pp. 65–68
Leutenegger S, Chli M, Siegwart RY (2011) Brisk: Binary robust invariant scalable keypoints. In: 2011 International conference on computer vision (ICCV), IEEE, pp. 2548–2555
Leydier Y, Ouji A, LeBourgeois F, Emptoz H (2009) Towards an omnilingual word retrieval system for ancient manuscripts. Pattern Recognit 42:2089–2105
Liang Y, Fairhurst MC, Guest RM (2012) A synthesised word approach to word retrieval in handwritten documents. Pattern Recognit 45(12):4225–4236
Lindeberg T (1998) Feature detection with automatic scale selection. Int J Comput Vis 30(2):79–116
Litman R, Bronstein AM (2013) Learning spectral descriptors for deformable shape correspondence. IEEE Trans Pattern Anal Mach Intell 36:171–180
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:90–110
Manmatha R, Han C, Riseman E (1996) Word spotting: a new approach to indexing handwriting. In: IEEE computer vision and pattern recognition, pp. 631–637
Marti UV, Bunke H (2001) 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
Meyer M, Desbrun M, Schröder P, Bar A (2002) Discrete differential geometry operators for triangulated 2-manifolds. In: Proceedings of Visualization Mathematics, Springer, pp. 35–57
Moreno-Noguer F (2011) Deformation and illumination invariant feature point descriptor. In: Proceedings of computer vision and patteren recognition (CVPR), IEEE, pp. 1593–1600
Pinkall U, Polthier K (1993) Computing discrete minimal surfaces and their conjugates. Exp Math 2:15–36
Rath T, Manmatha R (2007) Word spotting for historical documents. Int J Doc Anal Recognit 9:139–152
Rath TM, Manmatha R (2003) Word image matching using dynamic time warping. In: 2003 IEEE computer society conference on computer vision and pattern recognition, 2003. Proceedings, IEEE vol. 2, pp. 521–527
Rodriguez J, Perronnin F (2008) Local gradient histogram features for word spotting in unconstrained handwritten documents. In: Proceedings of international conference on frontiers in handwriting recognition (ICFHR)
Rodriguez-Serrano J, Perronnin F (2012) A model-based sequence similarity with application to handwritten word spotting. IEEE Trans Pattern Anal Mach Intell 34:2108–2120
Rothacker L, Fink GA, Banerjee P, Bhattacharya U, Chaudhuri BB (2013) Bag-of-features hmms for segmentation-free bangla word spotting. In: Proceedings of the 4th international workshop on multilingual OCR ACM
Rusinol M, Aldavert D, Toledo R, Llados J (2011) Browsing heterogeneous document collections by a segmentation-free word spotting method. In: Proceedings of international conference on document analysis and recognition (ICDAR), IEEE, pp. 63–67
Sarkar R, Das N, Basu S, Kundu M, Nasipuri M, Basu DK (2012) Cmaterdb1: a database of unconstrained handwritten Bangla and Bangla-English mixed script document image. Int J Doc Anal Recognit 15(1):71–83
Rusinol M, Aldavert D, T R, Llados J (2015) Efficient segmentation-free keyword spotting in historical document collections. Pattern Recognit 48(2):545–555
Shekhar R, Jawahar C (2012) Word image retrieval using bag of visual words. In: Proceedings of document analysis system (DAS), pp. 297–301
Sun J, Ovsjanikov M, Guibas L (2009) A concise and provably informative multiscale signature based on heat diffusion. Comput Graph Forum 28:1383–1392
Teraswa K, Tanake Y (2009) Slit style hog feature for document image word spotting. In: Proceedings of international conference of document analysis and recognition (ICDAR), IEEE, pp. 116–120
Zagoris K, Pratikakis I, Gatos B (2014) Segmentation-based historical handwritten word spotting using document-specific local features. In: Proceedings of international conference on frontiers in handwritten recognition (ICFHR), pp. 9–14
Zhang X, Pal U, Tan CL (2014) Segmentation-free keyword spotting for bangla handwritten documents. In: Proceedings of international conference on frontiers in handwritten recognition (ICFHR), pp. 381–386
Zhang X, Tan CL (2013) Segmentation-free keyword spotting for handwritten documents based on heat kernel signature. In: Proceedings of international conference of document analysis and recognition (ICDAR), IEEE, pp. 827–831
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Das, S., Mandal, S. Segmentation-free word spotting in historical Bangla handwritten document using Wave Kernel Signature. Pattern Anal Applic 23, 593–610 (2020). https://doi.org/10.1007/s10044-019-00823-1
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DOI: https://doi.org/10.1007/s10044-019-00823-1