Stammdaten

Titel: Automated bank cheque verification using image processing and deep learning methods
Untertitel:
Kurzfassung:

Automated bank cheque verification using image processing is an attempt to complement the present cheque truncation system, as well as to provide an alternate methodology for the processing of bank cheques with minimal human intervention. When it comes to the clearance of the bank cheques and monetary transactions, this should not only be reliable and robust but also save time which is one of the major factor for the countries having large population. In order to perform the task of cheque verification, we developed a tool which acquires the cheque leaflet key components, essential for the task of cheque clearance using image processing and deep learning methods. These components include the bank branch code, cheque number, legal as well as courtesy amount, account number, and signature patterns. our innovation aims at benefiting the banking system by re-innovating the other competent cheque-based monetary transaction system which requires automated system intervention. For this research, we used institute of development and research in banking technology (IDRBT) cheque dataset and deep learning based convolutional neural networks (CNN) which gave us an accuracy of 99.14% for handwritten numeric character recognition. It resulted in improved accuracy and precise assessment of the handwritten components of bank cheque. For machine printed script, we used MATLAB in-built OCR method and the accuracy achieved is satisfactory (97.7%) also for verification of Signature we have used Scale Invariant Feature Transform (SIFT) for extraction of features and Support Vector Machine (SVM) as classifier, the accuracy achieved for signature verification is 98.10%.

Schlagworte: Cheque truncation system; Image segmentation; Bank cheque clearance; Image feature extraction; Convolution neural network; Support vector machine; Scale invariant feature transform
Publikationstyp: Beitrag in Zeitschrift (Autorenschaft)
Art der Veröffentlichung Online Publikation
Erschienen in: Multimedia Tools and Applications
Multimedia Tools and Applications
zur Publikation
 ( )
Erscheinungsdatum: 06.10.2020
Titel der Serie: -
Bandnummer: -
Heftnummer: -
Erstveröffentlichung: Ja
Version: -
Seite: S. 1 - 32

Identifikatoren

ISBN: -
ISSN: 1573-7721
DOI: http://dx.doi.org/10.1007/s11042-020-09818-1
AC-Nummer: -
Homepage: https://link.springer.com/article/10.1007%2Fs11042-020-09818-1
Open Access
  • Online verfügbar (nicht Open Access)

Zuordnung

Organisation Adresse
Fakultät für Technische Wissenschaften
 
Institut für Informationstechnologie
Universitaetsstr. 65-67
9020 Klagenfurt am Wörthersee
Österreich
   martina.steinbacher@uni-klu.ac.at
http://itec.aau.at/
zur Organisation
Universitaetsstr. 65-67
AT - 9020  Klagenfurt am Wörthersee

Kategorisierung

Sachgebiete
  • 1020 - Informatik
Forschungscluster Kein Forschungscluster ausgewählt
Zitationsindex
  • Science Citation Index Expanded (SCI Expanded)
Informationen zum Zitationsindex: Master Journal List
Peer Reviewed
  • Ja
Publikationsfokus
  • Science to Science (Qualitätsindikator: I)
Klassifikationsraster der zugeordneten Organisationseinheiten:
Arbeitsgruppen
  • Distributed Multimedia Systems

Kooperationen

Organisation Adresse
Lovely Professional University
Jalandhar - Delhi G.T. Road
Phagwara, Punjab
Indien
https://www.lpu.in/
Jalandhar - Delhi G.T. Road
IN  Phagwara, Punjab

Beiträge der Publikation

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