Skip navigation
Please use this identifier to cite or link to this item:
Title: Bag-of-visual-words for signature-based multi-script document retrieval
Authors: Mandal R.
Pratim Roy, Partha
Pal U.
Blumenstein M.
Published in: Neural Computing and Applications
Abstract: An end-to-end architecture for multi-script document retrieval using handwritten signatures is proposed in this paper. The user supplies a query signature sample, and the system exclusively returns a set of documents that contain the query signature. In the first stage, a component-wise classification technique separates the potential signature components from all other components. A bag-of-visual-words powered by SIFT descriptors in a patch-based framework is proposed to compute the features and a support vector machine (SVM)-based classifier was used to separate signatures from the documents. In the second stage, features from the foreground (i.e., signature strokes) and the background spatial information (i.e., background loops, reservoirs etc.) were combined to characterize the signature object to match with the query signature. Finally, three distance measures were used to match a query signature with the signature present in target documents for retrieval. The ‘Tobacco’ (The Legacy Tobacco Document Library (LTDL). University of California, San Francisco, 2007. document database and an Indian script database containing 560 documents of Devanagari (Hindi) and Bangla scripts were used for the performance evaluation. The proposed system was also tested on noisy documents, and the promising results were obtained. A comparative study shows that the proposed method outperforms the state-of-the-art approaches. © 2018, The Natural Computing Applications Forum.
Citation: Neural Computing and Applications (2019), 31(10): 6223-6247
Issue Date: 2019
Publisher: Springer London
Keywords: Bag-of-visual-words
Content-based document retrieval
Logo retrieval
Signature retrieval
Spatial pyramid matching
ISSN: 9410643
Author Scopus IDs: 54410932900
Author Affiliations: Mandal, R., School of Information and Communication Technology, Griffith University, Gold Coast, QLD, Australia
Roy, P.P., Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India
Pal, U., Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, Kolkata, India
Blumenstein, M., School of Software, University of Technology Sydney, Sydney, Australia
Corresponding Author: Mandal, R.; School of Information and Communication Technology, Griffith UniversityAustralia; email:
Appears in Collections:Journal Publications [CS]

Files in This Item:
There are no files associated with this item.
Show full item record

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.