JOURNAL ARTICLE

Android based application for visually impaired using deep learning approach

Haslinah Mohd NasirNoor Mohd Ariff BrahinMai Mariam Mohamed AminuddinMohd Syafiq MispanMohd Faizal Zulkifli

Year: 2021 Journal:   IAES International Journal of Artificial Intelligence Vol: 10 (4)Pages: 879-879   Publisher: Institute of Advanced Engineering and Science (IAES)

Abstract

<span>People with visually impaired had difficulties in doing activities related to environment, social and technology. Furthermore, they are having issues with independent and safe in their daily routine. This research propose deep learning based visual object recognition model to help the visually impaired people in their daily basis using the android application platform. This research is mainly focused on the recognition of the money, cloth and other basic things to make their life easier. The convolution neural network (CNN) based visual recognition model by TensorFlow object application programming interface (API) that used single shot detector (SSD) with a pre-trained model from Mobile V2 is developed at Google dataset. Visually impaired persons capture the image and will be compared with the preloaded image dataset for dataset recognition. The verbal message with the name of the image will let the blind used know the captured image. The object recognition achieved high accuracy and can be used without using internet connection. The visually impaired specifically are largely benefited by this research.</span>

Keywords:
Computer science Visually impaired Android (operating system) Artificial intelligence Convolutional neural network Deep learning Cognitive neuroscience of visual object recognition Mobile device Computer vision Classifier (UML) Machine learning Human–computer interaction Feature extraction World Wide Web

Metrics

16
Cited By
1.23
FWCI (Field Weighted Citation Impact)
17
Refs
0.81
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Currency Recognition and Detection
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Retinal Imaging and Analysis
Health Sciences →  Medicine →  Radiology, Nuclear Medicine and Imaging

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