AUT LibraryAUT
View Item 
  •   Open Theses & Dissertations
  • Masters Theses
  • View Item
  •   Open Theses & Dissertations
  • Masters Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Banknote Serial Number Recognition Using Deep Learning

Ma, Xin
Thumbnail
View/Open
Thesis (3.231Mb)
Permanent link
http://hdl.handle.net/10292/13337
Metadata
Show full metadata
Abstract
Deep learning has been broadly applied to pattern classification, object detection, image segmentation, speech recognition, and other fields in recent years. Convolutional neural networks take dominant role in the field of deep learning, which has excellent characteristics that traditional machine learning algorithms cannot reach. The problem of character recognition has also been extensively studied in recent years, whose scope is much wide, including license plate recognition, handwriting recognition, bank check, and handwriting recognition for postcodes on envelop, etc.

According to the current circulation banknote in New Zealand, this thesis applies deep learning to the character recognition of serial numbers on banknotes. The data samples used in this thesis are the images from the sixth edition of New Zealand banknote, which have been preprocessed with labelling, augmentation, scaling, and transformation, etc. The algorithms based on deep learning are proposed which have the stability for the serial number recognition in complex backgrounds.

In this thesis, a pipeline of deep neural networks is constructed for character recognition of banknote serial numbers. Since high reliability is more important than accuracy in financial applications, DenseNet is proposed as the primary classifier, the scaling transformation of SegLink is employed to locate the characters, the detection rate is up to 95.80%. A convolutional neural network with residual attention model is proposed for serial number recognition, the precision reaches up to 97.09%.
Keywords
Banknote; Deep Learning; Object detection; Attention Model
Date
2020
Item Type
Thesis
Supervisor(s)
Yan, Wei Qi
Degree Name
Master of Computer and Information Sciences
Publisher
Auckland University of Technology

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library

 

 

Browse

Open Theses & DissertationsTitlesAuthorsDateThesis SupervisorMasters ThesesTitlesAuthorsDateThesis Supervisor

Alternative metrics

 

Statistics

For this itemFor all Open Theses & Dissertations

Share

 
Follow @AUT_SC

Contact Us
  • Admin

Hosted by Tuwhera, an initiative of the Auckland University of Technology Library