Real-Time Currency Detection and Voice Feedback for Visually Impaired Individuals

• URL: http://arxiv.org/abs/2510.20267v1
• Date: Thu, 23 Oct 2025 06:48:04 GMT
• Title: Real-Time Currency Detection and Voice Feedback for Visually Impaired Individuals
• Authors: Saraf Anzum Shreya, MD. Abu Ismail Siddique, Sharaf Tasnim,
• Abstract summary: This paper presents a real-time currency detection system designed to assist visually impaired individuals.<n>The proposed model is trained on a dataset containing 30 classes of notes and coins, representing 3 types of currency: US dollar (USD), Euro (EUR), and Bangladeshi taka (BDT)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Technologies like smartphones have become an essential in our daily lives. It has made accessible to everyone including visually impaired individuals. With the use of smartphone cameras, image capturing and processing have become more convenient. With the use of smartphones and machine learning, the life of visually impaired can be made a little easier. Daily tasks such as handling money without relying on someone can be troublesome for them. For that purpose this paper presents a real-time currency detection system designed to assist visually impaired individuals. The proposed model is trained on a dataset containing 30 classes of notes and coins, representing 3 types of currency: US dollar (USD), Euro (EUR), and Bangladeshi taka (BDT). Our approach uses a YOLOv8 nano model with a custom detection head featuring deep convolutional layers and Squeeze-and-Excitation blocks to enhance feature extraction and detection accuracy. Our model has achieved a higher accuracy of 97.73%, recall of 95.23%, f1-score of 95.85% and a mean Average Precision at IoU=0.5 (mAP50(B)) of 97.21\%. Using the voice feedback after the detection would help the visually impaired to identify the currency. This paper aims to create a practical and efficient currency detection system to empower visually impaired individuals independent in handling money.

Related papers

• Development of a Neural Network Model for Currency Detection to aid visually impaired people in Nigeria [0.0]
  We build a custom dataset of 3,468 images, which was subsequently used to train an SSD neural network model.<n>The proposed system can accurately identify Nigerian cash, thereby streamlining commercial transactions.
  arXiv  Detail & Related papers  (2025-08-25T13:27:27Z)
• 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)<n>A dataset comprising 50,334 images was collected, preprocessed, and used to train a CNN model optimized for high performance classification.<n>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.<n>A custom-created dataset of images of Sri Lankan currency notes was used to fine-tune an EfficientDet model.<n>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)
• Uncertainty Estimation for 3D Object Detection via Evidential Learning [63.61283174146648]
  We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye View representations in the 3D detector. We demonstrate both the efficacy and importance of these uncertainty estimates on identifying out-of-distribution scenes, poorly localized objects, and missing (false negative) detections.
  arXiv  Detail & Related papers  (2024-10-31T13:13:32Z)
• Real-time Yemeni Currency Detection [0.49109372384514843]
  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.
  arXiv  Detail & Related papers  (2024-06-18T19:57:15Z)
• Efficient Verification-Based Face Identification [50.616875565173274]
  We study the problem of performing face verification with an efficient neural model $f$. Our model leads to a substantially small $f$ requiring only 23k parameters and 5M floating point operations (FLOPS) We use six face verification datasets to demonstrate that our method is on par or better than state-of-the-art models.
  arXiv  Detail & Related papers  (2023-12-20T18:08:02Z)
• Banknote Recognition for Visually Impaired People (Case of Ethiopian note) [0.0]
  We developed an Android and IOS compatible mobile application with a model that achieved 98.9% classification accuracy on our dataset. The application has a voice integrated feature that tells the type of the scanned currency in Amharic, the working language of Ethiopia.
  arXiv  Detail & Related papers  (2022-08-25T19:46:34Z)
• Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning [62.83590925557013]
  We learn a set of challenging partially-observed manipulation tasks from visual and audio inputs. Our proposed system learns these tasks by combining offline imitation learning from tele-operated demonstrations and online finetuning. In a set of simulated tasks, we find that our system benefits from using audio, and that by using online interventions we are able to improve the success rate of offline imitation learning by 20%.
  arXiv  Detail & Related papers  (2022-05-30T04:52:58Z)
• One-Shot Object Affordance Detection in the Wild [76.46484684007706]
  Affordance detection refers to identifying the potential action possibilities of objects in an image. We devise a One-Shot Affordance Detection Network (OSAD-Net) that estimates the human action purpose and then transfers it to help detect the common affordance from all candidate images. With complex scenes and rich annotations, our PADv2 dataset can be used as a test bed to benchmark affordance detection methods.
  arXiv  Detail & Related papers  (2021-08-08T14:53:10Z)
• Learnable Online Graph Representations for 3D Multi-Object Tracking [156.58876381318402]
  We propose a unified and learning based approach to the 3D MOT problem. We employ a Neural Message Passing network for data association that is fully trainable. We show the merit of the proposed approach on the publicly available nuScenes dataset by achieving state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
  arXiv  Detail & Related papers  (2021-04-23T17:59:28Z)
• Automatic Counting and Identification of Train Wagons Based on Computer Vision and Deep Learning [70.84106972725917]
  The proposed solution is cost-effective and can easily replace solutions based on radiofrequency identification (RFID) The system is able to automatically reject some of the train wagons successfully counted, as they have damaged identification codes.
  arXiv  Detail & Related papers  (2020-10-30T14:56:54Z)

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.