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Unsupervised Word Spotting in Historical Handwritten Document Images using Document-oriented Local Features.

Authors
  • Zagoris, Konstantinos
  • Pratikakis, Ioannis
  • Gatos, Basilis
Type
Published Article
Journal
IEEE Transactions on Image Processing
Publisher
Institute of Electrical and Electronics Engineers
Publication Date
May 03, 2017
Identifiers
DOI: 10.1109/TIP.2017.2700721
PMID: 28475054
Source
Medline
License
Unknown

Abstract

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that it relies upon document-oriented local features which take into account information around representative keypoints as well a matching process that incorporates spatial context in a local proximity search without using any training data. Experimental results on four historical handwritten datasets for two different scenarios (segmentation-based and segmentation-free) using standard evaluation measures show the improved performance achieved by the proposed methodology.

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