An Intelligent Paper Currency Recognition System

AbstractPaper currency recognition (PCR) is an important area of pattern recognition. A system for the recognition of paper currency is one kind of intelligent system which is a very important need of the current automation systems in the modern world of today. It has various potential applications including electronic banking, currency monitoring systems, money exchange machines, etc. This paper proposes an automatic paper currency recognition system for paper currency. A method of recognizing paper currencies has been introduced. This is based on interesting features and correlation between images. It uses Radial Basis Function Network for classification. The method uses the case of Saudi Arabian paper currency as a model. The method is quite reasonable in terms of accuracy. The system deals with 110 images, 10 of which are tilted with an angle less than 15o. The rest of the currency images consist of mixed including noisy and normal images 50 each. It uses fourth series (1984–2007) of currency issued by Saudi Arabian Monetary Agency (SAMA) as a model currency under consideration. The system produces accuracy of recognition as 95.37%, 91.65%, and 87.5%, for the Normal Non-Tilted Images, Noisy Non-Tilted Images, and Tilted Images respectively. The overall Average Recognition Rate for the data of 110 images is computed as 91.51%. The proposed algorithm is fully automatic and requires no human intervention. The proposed technique produces quite satisfactory results in terms of recognition and efficiency

Currency security and forensics: a survey

By its definition, the word currency refers to an agreed medium for exchange, a nation’s currency is the formal medium enforced by the elected governing entity. Throughout history, issuers have faced one common threat: counterfeiting. Despite technological advancements, overcoming counterfeit production remains a distant future. Scientific determination of authenticity requires a deep understanding of the raw materials and manufacturing processes involved. This survey serves as a synthesis of the current literature to understand the technology and the mechanics involved in currency manufacture and security, whilst identifying gaps in the current literature. Ultimately, a robust currency is desire

Exploiting multimedia in creating and analysing multimedia Web archives

The data contained on the web and the social web are inherently multimedia and consist of a mixture of textual, visual and audio modalities. Community memories embodied on the web and social web contain a rich mixture of data from these modalities. In many ways, the web is the greatest resource ever created by human-kind. However, due to the dynamic and distributed nature of the web, its content changes, appears and disappears on a daily basis. Web archiving provides a way of capturing snapshots of (parts of) the web for preservation and future analysis. This paper provides an overview of techniques we have developed within the context of the EU funded ARCOMEM (ARchiving COmmunity MEMories) project to allow multimedia web content to be leveraged during the archival process and for post-archival analysis. Through a set of use cases, we explore several practical applications of multimedia analytics within the realm of web archiving, web archive analysis and multimedia data on the web in general

Nigeria Paper Currency Serial Number Pattern Recognition System for Crimes Control

Only secured and conducive environment void of robbery, kidnapping, fake currency and all forms of insurgencies will foster production and distribution of goods, investment and saving that enhance national economic growth and development. This is a mirage in a country generally believed and tagged the giant of African; Nigeria. Crime, in whatever name or nomenclature, has a significant negative impact on the welfare and economy prosperities of our society. The urge to get rich promotes Crime like armed robbery, kidnapping for ransom and production of counterfeit banknotes to mention but a few. Innocent people have suffered psychological distress, fear, anger, depression, physical harm, financial loss and in most cases untimely death during the operations by these hoodlums. Banks, Cash-In-Transit Vehicle, and ATM points are often robbed by gangs in search for paper currency. Kidnappers as well demand for paper currency as ransom while some other gangs are involved in the production of counterfeit banknotes so as to enrich themselves no minding the negative effect on the nation’s economy.  The banknotes collected during the operations by the hoodlums are taken to banks. Yet, the banks will not detect or recognize any of these notes which attest to the fact that our system lacks check and balance. The system is very porous without a recourse to this era of technology when machine is trained to do virtually everything for our convenience. Currency as an entity has a unique identification number. The identification number is an alphanumeric currency issuance of about 10 digits comprises two (2) capital letters and eight (8) numbers usually positioned at a strategic location on either front or back of the 5, 10, 20, 50, 100, 200, 500 and 1000 naira notes. It is a reliable and intelligent system developed to track banknotes unique identifiers numbers- serial numbers, in order to control financial related crimes. Keywords: Nigeria Paper Currency Serial Number, Pattern Recognition DOI: 10.7176/IKM/11-3-04 Publication date: April 30th 202

Aplikasi Deteksi Nilai Uang Pada Mata Uang Indonesia Dengan Metode Feature Matching

Banknote can\u27t be detected and recognized by blind people.Application for banknote recognition, which can run on Androidsmartphone with camera, is made in this paper to help blind people.Indonesian Rupiah(IDR) is used as a working example. This doesnot require any communication with remote server, and all thenecessary computations take place on the phone itself. In thisapplication, user can tak picture of the banknote with double tapon the screen, and the results will be announced by voice. Thesystem relies on computer vision algorithm, such as featuredetection, feature description, and matching. Each application ofORB, SURF, and SIFT is applied in matching captured banknoteimages to template images. To improve the confidence of thebanknote recognition, homography is used to filter the featurematching results.The systems evaluate the performance on 700 images and report anaccuracy of 93.14% using SURF, 92.57% using SIFT, and 89.17%using OR

Currency recognition using a smartphone: Comparison between color SIFT and gray scale SIFT algorithms

AbstractBanknote recognition means classifying the currency (coin and paper) to the correct class. In this paper, we developed a dataset for Jordanian currency. After that we applied automatic mobile recognition system using a smartphone on the dataset using scale-invariant feature transform (SIFT) algorithm. This is the first attempt, to the best of the authors knowledge, to recognize both coins and paper banknotes on a smartphone using SIFT algorithm. SIFT has been developed to be the most robust and efficient local invariant feature descriptor. Color provides significant information and important values in the object description process and matching tasks. Many objects cannot be classified correctly without their color features. We compared between two approaches colored local invariant feature descriptor (color SIFT approach) and gray image local invariant feature descriptor (gray SIFT approach). The evaluation results show that the color SIFT approach outperforms the gray SIFT approach in terms of processing time and accuracy

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Robust and Effective Banknote Recognition Model for Aiding Visual Impaired People

Visual disabled Ethiopians find great difficulty in recognizing banknotes. Each Ethiopian banknote has an identical feel, with no Braille markings, irregular edges, or other tangible features that make it easily recognizable by blind persons. In Ethiopia, there's only one device available that will assist blind people to acknowledge their notes. Internationally, there are devices available; however, they're expensive, complex, and haven't been developed to cater to Ethiopian currency. Because of these facts, visually impaired people may suffer from recognizing each folding money. This fact necessitates a higher authentication and verification system that will help visually disabled people to simply identify and recognize the banknotes. This paper presents a denomination-specific component-based framework for a banknote recognition system. Within the study, the dominant color of the banknotes was first identified and so the exclusive feature for every denomination-specific ROI was calculated. Finally, the Colour-Momentum, dominant color, and GLCM features were calculated from each denomination-specific ROI. Designing the recognition system by thereby considering the denomination-specific ROI is simpler as compared to considering the entire note in collecting more class-specific information and robust in copying with partial occlusion and viewpoint changes. The performance of the proposed model was verified by using a larger dataset of which containing banknotes in several conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves a 98% recognition rate on our challenging datasets