A Systematic Literature Review of the Mobile Application for Object Recognition for Visually Impaired People

Nowadays, mobile applications or also known as mobile apps are one of the most important sources of communication and gives a lot of benefits to a person. Mobile technology advancements have brought an excessive change in people's daily lifestyles which has led to high demand for developing software that can give benefit to a person that runs on a mobile device. Mobile apps are represented visually on a smartphone, which it is not user-friendly for visually impaired people. The number of visually impaired people are growing in each of the countries in the world. Most technologies are being developed to help people with visual impairment because they still have difficulties in using assistive technologies. This systematic literature review attempts to provide findings on the project for visually impaired people to recognize objects using their smartphones. Systematic Literature Review (SLR) method was used to collect and review the current state of analysis concerning object recognition for visually impaired people on mobile platforms. Overall, this research covered articles published in three databases which are ScienceDirect, IEEE Xplore and Google Scholar and the articles selected are between 2013 until 2019. This paper is based on the Kitchenham methodology where the keyword search and exclusion criteria to select the articles about the topic were used. After reading the total of 371 titles of articles based on keywords, 323 abstracts were examined thoroughly. Then, 21 full-text articles were chosen for the final review. The findings show the techniques, tools used and methods on how the system was implemented to recognize object.

[1]  Sukadev Meher,et al.  A Novel method for visually impaired using object recognition , 2015, 2015 International Conference on Communications and Signal Processing (ICCSP).

[2]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[3]  Barbara Kitchenham,et al.  Procedures for Performing Systematic Reviews , 2004 .

[4]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[5]  Robin Holt,et al.  The SAGE Dictionary of Qualitative Management Research , 2007 .

[6]  Mikolaj Leszczuk,et al.  Simple solution for public transport route number recognition based on visual information , 2013, 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA).

[7]  Akash Pooransingh,et al.  Automated Money Detection Application for Trinidad and Tobago Currency Notes , 2015 .

[8]  M. Ashraful Amin,et al.  Medicine Recognition from Colors and Text , 2019 .

[9]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[10]  Rodrigo C. Barros,et al.  Real-Time Detection of Pedestrian Traffic Lights for Visually-Impaired People , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).

[11]  L. J. Sankpal,et al.  VisualPal: A mobile app for object recognition for the visually impaired , 2015, 2015 International Conference on Computer, Communication and Control (IC4).

[12]  Hao Jiang,et al.  Computer vision and text recognition for assisting visually impaired people using Android smartphone , 2017, 2017 IEEE International Conference on Electro Information Technology (EIT).

[13]  Najmul Hasan,et al.  A Systematic Literature Review of the Application of Information Communication Technology for Visually Impaired People , 2016, International Journal of Disability Management.

[14]  Mohammed A.-M. Salem,et al.  Android-based object recognition for the visually impaired , 2013, 2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS).

[15]  Zheng Li,et al.  PreSight: Enabling Real-Time Detection of Accessibility Problems on Sidewalks , 2017, 2017 14th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[16]  Soyeong Lee,et al.  CNN-Based Drug Recognition and Braille Embosser System for the Blind , 2018, J. Comput. Sci. Eng..

[17]  Miguel Cazorla,et al.  Enhancing perception for the visually impaired with deep learning techniques and low-cost wearable sensors , 2020, Pattern Recognit. Lett..

[18]  Eun Yi Kim,et al.  A Vision-Based Wayfinding System for Visually Impaired People Using Situation Awareness and Activity-Based Instructions , 2017, Sensors.

[19]  Khaled Ahmed Nagaty,et al.  Android Application to Assist Visually Impaired with Outfit Coordination , 2014 .

[20]  Faouzi Benzarti,et al.  Object detection and identification for blind people in video scene , 2015, 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA).

[21]  J. G. McGuire.  What in the world? , 1996, The Journal of school health.

[22]  Antonio Puliafito,et al.  Building TensorFlow Applications in Smart City Scenarios , 2017, 2017 IEEE International Conference on Smart Computing (SMARTCOMP).

[23]  Pei-Ying Chiang,et al.  Simple Smartphone-Based Guiding System for Visually Impaired People , 2017, Sensors.

[24]  Saleh Shadi,et al.  Outdoor Navigation for Visually Impaired based on Deep Learning , 2019 .