currency-recognition

Drishti aims to help a visually impaired or a blind person in their everyday life tasks such as Recognizing Currency Notes, reading text from live images, and sharing their live locations with their loved ones.

A web-based demo app to detect and classify Indian currency notes using a Roboflow-trained instance segmentation model. Built for educational and research demonstration, this app showcases real-time currency recognition directly from images or live camera input.

Aplikasi inovatif untuk tunanetra yang membantu menavigasi lingkungan dengan fitur deteksi objek, uang, warna, bahasa isyarat, dan dokumen. Dirancang untuk meningkatkan kemandirian dan aksesibilitas, Alfabel menggunakan teknologi cerdas untuk memudahkan kehidupan sehari-hari.

AI-powered camera vision system for BEACON, a smart assistive cap for visually impaired users. Uses YOLOv11, OpenCV, and ESP32-CAM for real-time object detection, crosswalk recognition, currency identification, fall detection, and voice-based navigation alerts.

Real-time Sri Lankan coin detection and classification system using YOLOv8. Accurately identifies various Sri Lankan coin denominations using deep learning and computer vision.

AI-powered Pakistani banknote recognition app using RT-DETR with Android, English/Urdu voice feedback, and accessibility features.

CNN-based Yemeni banknote classification using MobileNet, DenseNet121, EfficientNetV2 with custom dataset & augmentations.

Vietnamese banknote recognition using deep learning and image classification. Identifies denominations from photos in real time.

YOLOv8-based instance segmentation for Indian currency notes (₹10-₹2000). Includes custom dataset, training scripts, inference, evaluation, and visualization tools for pixel-level masks

Automatic recognition of Ghanaian banknotes (GH₵1–GH₵200) using a MobileNetV2 CNN, served via a Flask REST API and a React frontend. Upload a note image and get an instant denomination prediction with confidence score.