Iranian cashes recognition using mobile

In economical societies of today, using cash is an inseparable aspect of human life. People use cashes for marketing, services, entertainments, bank operations and so on. This huge amount of contact with cash and the necessity of knowing the monetary value of it caused one of the most challenging problems for visually impaired people. In this paper we propose a mobile phone based approach to identify monetary value of a picture taken from cashes using some image processing and machine vision techniques. While the developed approach is very fast, it can recognize the value of cash by average accuracy of about 95% and can overcome different challenges like rotation, scaling, collision, illumination changes, perspective, and some others.Comment: arXiv #13370

A Novel Method for Banknote Recognition Using a Combined Histogram of Oriented Gradients and Scale-Invariant Feature Transform

Automated banknote recognition systems are essential for people with visual impairments who face challenges distinguishing between different currency denominations. This study presents a novel method aimed at helping blind people identify banknotes from three different countries (Egypt, Saudi Arabia, and the United States of America) by using a proposed feature detection algorithm. Our proposed system has two main stages: the proposed algorithm uses the Speeded-UP Robust Features (SURF) algorithm for key point detection, as it is fast and robust to variations in geometry and lighting. Then, it extracts features using the scale-invariant feature transform (SIFT) and histogram of oriented gradients (HOG) algorithms, which are scale invariant. This algorithm aims to overcome the limitations of both the SURF and SIFT algorithms and reduce the average response time and computational cost of the SIFT and HOG algorithms. We developed a banknote dataset with 12 classes for three countries. The accuracy of the banknote recognition was 99.2%. The performance of the proposed dataset was compared with that of the global Kaggle Egyptian dataset, resulting in 98.9% accuracy

Euro Banknote Recognition System for Blind People

[EN] This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter) dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.The work was supported by the project from the Generalitat Valenciana under the number GV/2014/015-Emergency projects.Dunai, L.; Chillarón-Pérez, M.; Peris Fajarnes, G.; Lengua, I. (2017). Euro Banknote Recognition System for Blind People. Sensors. 17(1)(184):1-14. https://doi.org/10.3390/s17010184S11417(1)18

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

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 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

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

ARTIFICIAL NEURAL NETWORK TO RECOGNIZE AN INDIAN CURRENCY NOTE USING UNIQUE IDENTIFICATION MARK

Artificial neural network has a vast application and has been successfully applied to a broad spectrum of data intensive application such as financial, data mining, medical, operational analysis, industrial, science etc. This increase in application is due to its ability to solve problem where the relationship are quite dynamic or non linear. Therefore in this paper we have used ANN to recognize Indian currency note using one special feature of Indian currency note known as Identification Mark (I.D mark). The I.D mark is of different shape for different currency note except for Rs 10 where there is no identification mark present. In the proposed method first we define a window size based on the common region where there is I.D mark. Then based on the window size we have segmented the I.D mark from the currency note. After this Fourier Descriptor is used to extract the feature from the segmented portion. Then the identification of this extracted feature is done by using ANN

Banknote Authentication and Medical Image Diagnosis Using Feature Descriptors and Deep Learning Methods

Banknote recognition and medical image analysis have been the foci of image processing and pattern recognition research. As counterfeiters have taken advantage of the innovation in print media technologies for reproducing fake monies, hence the need to design systems which can reassure and protect citizens of the authenticity of banknotes in circulation. Similarly, many physicians must interpret medical images. But image analysis by humans is susceptible to error due to wide variations across interpreters, lethargy, and human subjectivity. Computer-aided diagnosis is vital to improvements in medical analysis, as they facilitate the identification of findings that need treatment and assist the expert’s workflow. Thus, this thesis is organized around three such problems related to Banknote Authentication and Medical Image Diagnosis. In our first research problem, we proposed a new banknote recognition approach that classifies the principal components of extracted HOG features. We further experimented on computing HOG descriptors from cells created from image patch vertices of SURF points and designed a feature reduction approach based on a high correlation and low variance filter. In our second research problem, we developed a mobile app for banknote identification and counterfeit detection using the Unity 3D software and evaluated its performance based on a Cascaded Ensemble approach. The algorithm was then extended to a client-server architecture using SIFT and SURF features reduced by Bag of Words and high correlation-based HOG vectors. In our third research problem, experiments were conducted on a pre-trained mobile app for medical image diagnosis using three convolutional layers with an Ensemble Classifier comprising PCA and bagging of five base learners. Also, we implemented a Bidirectional Generative Adversarial Network to mitigate the effect of the Binary Cross Entropy loss based on a Deep Convolutional Generative Adversarial Network as the generator and encoder with Capsule Network as the discriminator while experimenting on images with random composition and translation inferences. Lastly, we proposed a variant of the Single Image Super-resolution for medical analysis by redesigning the Super Resolution Generative Adversarial Network to increase the Peak Signal to Noise Ratio during image reconstruction by incorporating a loss function based on the mean square error of pixel space and Super Resolution Convolutional Neural Network layers

Invariant Image-Based Currency Denomination Recognition Using Local Entropy and Range Filters

We perform image-based denomination recognition of the Pakistani currency notes. There are a total of seven different denominations in the current series of Pakistani notes. Apart from color and texture, these notes differ from one another mainly due to their aspect ratios. Our aim is to exploit this single feature to attain an image-based recognition that is invariant to the most common image variations found in currency notes images. Among others, the most notable image variations are caused by the difference in positions and in-plane orientations of the currency notes in images. While most of the proposed methods for currency denomination recognition only focus on attaining higher recognition rates, our aim is more complex, i.e., attaining a high recognition rate in the presence of image variations. Since, the aspect ratio of a currency note is invariant to such differences, an image-based recognition of currency notes based on aspect ratio is more likely to be translation- and rotation-invariant. Therefore, we adapt a two step procedure that first extracts a currency note from the homogeneous image background via local entropy and range filters. Then, the aspect ratio of the extracted currency note is calculated to determine its denomination. To validate our proposed method, we gathered a new dataset with the largest and most diverse collection of Pakistani currency notes, where each image contains either a single or multiple notes at arbitrary positions and orientations. We attain an overall average recognition rate of 99% which is very encouraging for our method, which relies on a single feature and is suited for real-time applications. Consequently, the method may be extended to other international and historical currencies, which makes it suitable for business and digital humanities application