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  • Signature Segmentation and Recognition from Scanned Documents

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    91641_1.pdf (424.9Kb)
    Author(s)
    Mandal, Ranju
    Roy, Partha Pratim
    Pal, Umapada
    Blumenstein, Michael
    Griffith University Author(s)
    Blumenstein, Michael M.
    Mandal, Ranju
    Year published
    2013
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    Abstract
    Signature as a query is important for content-based document image retrieval from a scanned document repository. This paper presents a two-stage approach towards automatic signature segmentation and recognition from scanned document images. In the first stage, signature blocks are segmented from the document using word-wise component extraction and classification. Gradient based features are extracted from each component at the word level to perform the classification task. In the 2nd stage, SIFT (Scale-Invariant Feature Transform) descriptors and Spatial Pyramid Matching (SPM)-based approaches are used for signature ...
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    Signature as a query is important for content-based document image retrieval from a scanned document repository. This paper presents a two-stage approach towards automatic signature segmentation and recognition from scanned document images. In the first stage, signature blocks are segmented from the document using word-wise component extraction and classification. Gradient based features are extracted from each component at the word level to perform the classification task. In the 2nd stage, SIFT (Scale-Invariant Feature Transform) descriptors and Spatial Pyramid Matching (SPM)-based approaches are used for signature recognition. Support Vector Machines (SVMs) are employed as the classifier for both levels in this experiment. The experiments are performed on the publicly available "Tobacco-800" and GPDS [1] datasets and the results obtained from the experiments are promising.
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    Conference Title
    2013 13TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS (ISDA)
    Publisher URI
    http://www.mirlabs.net/isda13/proceedings/
    DOI
    https://doi.org/10.1109/ISDA.2013.6920712
    Copyright Statement
    © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Pattern Recognition and Data Mining
    Publication URI
    http://hdl.handle.net/10072/65371
    Collection
    • Conference outputs

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