Real-time Yemeni Currency Detection

• URL: http://arxiv.org/abs/2406.13034v1
• Date: Tue, 18 Jun 2024 19:57:15 GMT
• Title: Real-time Yemeni Currency Detection
• Authors: Edrees AL-Edreesi, Ghaleb Al-Gaphari,
• Abstract summary: Banknote recognition is a major problem faced by visually Challenged people. This paper presents a real-time Yemeni currency detection system for visually impaired persons.
• Score: 0.49109372384514843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Banknote recognition is a major problem faced by visually Challenged people. So we propose a application to help the visually Challenged people to identify the different types of Yemenian currencies through deep learning technique. As money has a significant role in daily life for any business transactions, real-time detection and recognition of banknotes become necessary for a person, especially blind or visually impaired, or for a system that sorts the data. This paper presents a real-time Yemeni currency detection system for visually impaired persons. The proposed system exploits the deep learning approach to facilitate the visually impaired people to prosperously recognize banknotes. For real-time recognition, we have deployed the system into a mobile application.

Related papers

• BD Currency Detection: A CNN Based Approach with Mobile App Integration [1.2535250082638645]
  This study introduces an advanced currency recognition system utilizing Convolutional Neural Networks (CNNs) A dataset comprising 50,334 images was collected, preprocessed, and used to train a CNN model optimized for high performance classification. The trained model achieved an accuracy of 98.5%, surpassing conventional based currency recognition approaches.
  arXiv  Detail & Related papers  (2025-02-25T07:13:43Z)
• Money Recognition for the Visually Impaired: A Case Study on Sri Lankan Banknotes [0.0]
  This research proposes a user-friendly stand-alone system for the identification of Sri Lankan currency notes. A custom-created dataset of images of Sri Lankan currency notes was used to fine-tune an EfficientDet model. The model achieved 0.9847 AP on the validation dataset and performs exceptionally well in real-world scenarios.
  arXiv  Detail & Related papers  (2025-02-20T05:07:46Z)
• Time-Aware Face Anti-Spoofing with Rotation Invariant Local Binary Patterns and Deep Learning [50.79277723970418]
  imitation attacks can lead to erroneous identification and subsequent authentication of attackers. Similar to face recognition, imitation attacks can also be detected with Machine Learning. We propose a novel approach that promises high classification accuracy by combining previously unused features with time-aware deep learning strategies.
  arXiv  Detail & Related papers  (2024-08-27T07:26:10Z)
• Understanding How Blind Users Handle Object Recognition Errors: Strategies and Challenges [10.565823004989817]
  This paper presents a study aimed at understanding blind users' interaction with object recognition systems for identifying and avoiding errors. We conducted a user study involving 12 blind and low-vision participants. We gained insights into users' experiences, challenges, and strategies for identifying errors in camera-based assistive technologies and object recognition systems.
  arXiv  Detail & Related papers  (2024-08-06T17:09:56Z)
• Synthetic Counterfactual Faces [1.3062016289815055]
  We build a generative AI framework to construct targeted, counterfactual, high-quality synthetic face data. Our pipeline has many use cases, including face recognition systems sensitivity evaluations and image understanding system probes. We showcase the efficacy of our face generation pipeline on a leading commercial vision model.
  arXiv  Detail & Related papers  (2024-07-18T22:22:49Z)
• Real-Time Pill Identification for the Visually Impaired Using Deep Learning [31.747327310138314]
  This paper explores the development and implementation of a deep learning-based mobile application designed to assist blind and visually impaired individuals in real-time pill identification. The application aims to accurately recognize and differentiate between various pill types through real-time image processing on mobile devices.
  arXiv  Detail & Related papers  (2024-05-08T03:18:46Z)
• Robust Pedestrian Detection via Constructing Versatile Pedestrian Knowledge Bank [51.66174565170112]
  We propose a novel approach to construct versatile pedestrian knowledge bank. We extract pedestrian knowledge from a large-scale pretrained model. We then curate them by quantizing most representative features and guiding them to be distinguishable from background scenes.
  arXiv  Detail & Related papers  (2024-04-30T07:01:05Z)
• Integrating Language-Derived Appearance Elements with Visual Cues in Pedestrian Detection [51.66174565170112]
  We introduce a novel approach to utilize the strengths of large language models in understanding contextual appearance variations. We propose to formulate language-derived appearance elements and incorporate them with visual cues in pedestrian detection.
  arXiv  Detail & Related papers  (2023-11-02T06:38:19Z)
• Applications of Machine Learning in Detecting Afghan Fake Banknotes [0.0]
  The prevalence of fake currency in Afghanistan poses significant challenges and detrimentally impacts the economy. This paper introduces a method using image processing to identify counterfeit Afghan banknotes by analyzing specific security features. The Random Forest algorithm achieved exceptional accuracy of 99% in detecting fake Afghan banknotes.
  arXiv  Detail & Related papers  (2023-05-24T05:39:46Z)
• Robustness Disparities in Face Detection [64.71318433419636]
  We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models. Across all the datasets and systems, we generally find that photos of individuals who are $textitmasculine presenting$, of $textitolder$, of $textitdarker skin type$, or have $textitdim lighting$ are more susceptible to errors than their counterparts in other identities.
  arXiv  Detail & Related papers  (2022-11-29T05:22:47Z)
• A Novel Face-Anti Spoofing Neural Network Model For Face Recognition And Detection [0.0]
  Face Recognition (FR) systems are being used in a variety of applications, including road crossings, banking, and mobile banking. The widespread use of FR systems has raised concerns about the safety of face biometrics against spoofing attacks. This research proposes a face-anti-spoofing neural network model that outperforms existing models and has an efficiency of 0.89 percent.
  arXiv  Detail & Related papers  (2022-05-14T05:14:48Z)
• Harnessing Unrecognizable Faces for Face Recognition [87.80037162457427]
  We propose a measure of recognizability of a face image, implemented by a deep neural network trained using mostly recognizable identities. We show that accounting for recognizability reduces error rate of single-image face recognition by 58% at FAR=1e-5.
  arXiv  Detail & Related papers  (2021-06-08T05:25:03Z)
• Biometrics: Trust, but Verify [49.9641823975828]
  Biometric recognition has exploded into a plethora of different applications around the globe. There are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems.
  arXiv  Detail & Related papers  (2021-05-14T03:07:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.