A Light Deep Learning Based Method for Bank Serial Number Recognition

Ardian Umam, Jen-Hui Chuang, Dong Lin Li

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

1 Scopus citations

Abstract

A full stage bank serial number (SN) recognition system is proposed in this paper. We introduce Block-wise Prediction Networks (BPN) to treat the localization of an SN as block-wise binary classification, which can be considered as a coarse version of dense/pixel-wise prediction used in semantic segmentation. The benefits include short execution time, which is equal to 85.22 ms in CPU, and the use of global features instead of local features to improve the segmentation. Our system then separates the localized Region of Interest (RoI) into individual characters, and feeds them into softmax CNN classifier. Experimental results show that the proposed method can achieve 99.92% and 99.24% accuracy for character and SN of Renminbi (RMB), respectively, tested with 2,368 two sides images of 1,184 RMB bills.

Original languageEnglish
Title of host publicationVCIP 2018 - IEEE International Conference on Visual Communications and Image Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538644584
DOIs
StatePublished - 2 Jul 2018
Event33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018 - Taichung, Taiwan
Duration: 9 Dec 201812 Dec 2018

Publication series

NameVCIP 2018 - IEEE International Conference on Visual Communications and Image Processing

Conference

Conference33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018
CountryTaiwan
CityTaichung
Period9/12/1812/12/18

Keywords

  • Deep Learning
  • OCR
  • Region Proposer

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