Signature and logo detection using deep CNN for document image retrieval

Nabin Sharma, Ranju Mandal, Rabi Sharma, Umapada Pal, Michael Blumenstein

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

28 Citations (Scopus)

Abstract

Signature and logo as a query are important for content-based document image retrieval from a scanned document repository. This paper deals with signature and logo detection from a repository of scanned documents, which can be used for document retrieval using signature or logo information. A large intra-category variance among signature and logo samples poses challenges to traditional hand-crafted feature extraction-based approaches. Hence, the potential of deep learning-based object detectors namely, Faster R-CNN and YOLOv2 were examined for automatic detection of signatures and logos from scanned administrative documents. Four different network models namely ZF, VGG16, VGG-M, and YOLOv2 were considered for analysis and identifying their potential in document image retrieval. The experiments were conducted on the publicly available 'Tobacco-800' dataset. The proposed approach detects Signatures and Logos simultaneously. The results obtained from the experiments are promising and at par with the existing methods.

Original languageEnglish
Title of host publicationProceedings - 2018 16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages416-422
Number of pages7
ISBN (Electronic)9781538658758
DOIs
Publication statusPublished - 5 Dec 2018
Event16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018 - Niagara Falls, United States
Duration: 5 Aug 20188 Aug 2018

Publication series

NameProceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR
Volume2018-August
ISSN (Print)2167-6445
ISSN (Electronic)2167-6453

Conference

Conference16th International Conference on Frontiers in Handwriting Recognition, ICFHR 2018
Country/TerritoryUnited States
CityNiagara Falls
Period5/08/188/08/18

Keywords

  • Deep Learning
  • Document retrieval
  • Faster R-CNN
  • Logo detection
  • Signature detection

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