scholarly journals Deep Learning Based Indian Currency Detection for Visually Challenged using VGG16

2020 ◽  
Vol 9 (2) ◽  
pp. 769-972
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Visually Impaired ◽  
Convolution Neural Network ◽  
Visually Impaired People ◽  
Impaired People ◽  
Learning Technique ◽  
Different Types ◽  
Visually Challenged ◽  
Banknote Recognition

Banknote recognition is a major problem faced by visually Challenged people. So we propose a system to help the visually Challenged people to identify the different types of Indian currencies through deep learning technique. In our proposed project, bank notes with different positions are directly fed into VGG 16, a pretrained model of convolution neural network which extracts deep features. From our work the visually impaired people will be able to recognize different types if Indian Currencies.

Object Detection to Assist Visually Impaired People: A Deep Neural Network Adventure

2018 ◽  
pp. 500-510 ◽  
Author(s):  
Fereshteh S. Bashiri ◽  
Eric LaRose ◽  
Jonathan C. Badger ◽  
Roshan M. D’Souza ◽  
Zeyun Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Visually Impaired ◽  
Deep Neural Network ◽  
Visually Impaired People ◽  
Impaired People

scholarly journals INFERRING THE SCALE AND CONTENT OF A MAP USING DEEP LEARNING

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 17-24
Author(s):  
G. Touya ◽  
F. Brisebard ◽  
F. Quinton ◽  
A. Courtial
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Visually Impaired ◽  
The Other ◽  
Visually Impaired People ◽  
Impaired People ◽  
Tactile Maps

Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).

scholarly journals Real-Time Object Detection using Deep Learning and Open CV

2019 ◽  
Vol 8 (12S) ◽  
pp. 411-414
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Visually Impaired ◽  
Deep Neural Network ◽  
Raspberry Pi ◽  
Detailed Review ◽  
Real World Application ◽  
Satisfactory Performance ◽  
Visually Impaired People ◽  
Impaired People

The object detection is used in almost every realworld application such as autonomous traversal, visual system, face detection and even more. This paper aims at applying object detection technique to assist visually impaired people. It helps visually impaired people to know about the objects around them to enable them to walk free. A prototype has been implemented on a Raspberry PI 3 using OpenCV libraries, and satisfactory performance is achieved. In this paper, detailed review has been carried out on object detection using region – conventionaal neural network (RCNN) based learning systems for a real-world application. This paper explores the various process of detecting objects using various object detections methods and walks through detection including a deep neural network for SSD implemented using Caffe model.

scholarly journals Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible-Light Images Captured by Smartphone Cameras

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 63144-63161 ◽  
Author(s):  
Tuyen Danh Pham ◽  
Chanhum Park ◽  
Dat Tien Nguyen ◽  
Ganbayar Batchuluun ◽  
Kang Ryoung Park
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Visually Impaired ◽  
Visually Impaired People ◽  
Impaired People
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