Real-time Bangladeshi Currency Detection System for Visually Impaired Person

This paper presents a real-time Bangladeshi currency detection system for visually impaired persons. The proposed system exploits the image processing algorithms to facilitate the visually impaired people to prosperously recognize banknotes. The recent banknotes of Bangladesh have blind embossing or blind dots, which could be effective to recognize the value of the bill by touching. As the embossing fades away in the long-term used notes, detecting right value of the banknote using image processing algorithms could be considered as a challenging task. Particularly in Bangladesh, each banknote seems similar using the direct exertion of simplified image processing algorithms. In this paper, a recognition system was implemented that can detect Bangladeshi banknote in different viewpoints and scales. The detection system is also able to detect currency those are rumpled, decrepit or even worn. The detection system includes image preprocessing, image analysis and image recognition. To enhance the determination of currency recognition, the descriptor of an individual input scene is matched with various training images of the same category. After that, by analyzing their matching result it recognizes the currency with higher confidence. For real-time recognition, we have deployed the system into a mobile application.

[1]  Tony Lindeberg,et al.  Scale Invariant Feature Transform , 2012, Scholarpedia.

[2]  Md. Shahjahan,et al.  A currency recognition system using negatively correlated neural network ensemble , 2009, 2009 12th International Conference on Computers and Information Technology.

[3]  Fan-Hui Kong,et al.  Paper Currency Recognition using Gaussian Mixture Models Based on Structural Risk Minimization , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[4]  Xindong Wu,et al.  Object Detection With Deep Learning: A Review , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[5]  Zhang Yu.  A Fast Recognition System for Paper Currency Numbers , 2005 .

[6]  K. Yoshida,et al.  Design and implementation of a machine vision based but low cost stand alone system for real time counterfeit Bangladeshi bank notes detection , 2007, 2007 10th international conference on computer and information technology.

[7]  Liu Jia.  An Algorithm of Real-Time Paper Currency Recongnition , 2003 .

[8]  Song Ling,et al.  An Edge-Based Defect Detection Algorithm for Paper Currency , 2007 .

[9]  Hong Yan,et al.  Recognizing Bangladeshi Currency for Visually Impaired , 2014, ICMLC.

[10]  M. Kamruzzaman,et al.  A machine vision based automatic system for real time recognition and sorting of Bangladeshi bank notes. , 2008, 2008 11th International Conference on Computer and Information Technology.

[11]  Christopher Hunt,et al.  Notes on the OpenSURF Library , 2009 .

[12]  Horst Bischof,et al.  Efficient Maximally Stable Extremal Region (MSER) Tracking , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Lu Wu-yi.  Paper Currency Number Recognition System Based on ARM , 2010 .

[14]  Shah Md. Tanvir Siddiquee,et al.  Recognizing Hand-based Actions based on Hip-Joint centered Features using KINECT , 2017, 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE).

[15]  Munshi Alimushwan,et al.  Fake currency detection using image processing method , 2016 .

[16]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[17]  Sonya A. H. McMullen,et al.  Machine learning using template matching applied to object tracking in video data , 2019, Defense + Commercial Sensing.

[18]  Kpalma Kidiyo,et al.  A Survey of Shape Feature Extraction Techniques , 2008 .

[19]  Gary R. Bradski,et al.  ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.

[20]  Ahmed Al Marouf,et al.  4Y model: A novel approach for finger identification using KINECT , 2015, 2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS).

[21]  Raihan Uddin Ahmed,et al.  Image processing based Feature extraction of Bangladeshi banknotes , 2014, The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014).