VisualPal: A mobile app for object recognition for the visually impaired

Bagwan, Shagufta Md. Rafique; Sankpal, L. J.

doi:10.1109/ic4.2015.7375665

Cited by 20 publications

(5 citation statements)

References 5 publications

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“…Shagufta Md.Rafique Bagwan and L. J. Sankpal [7] proposed an android application that helps visually impaired people to recognize objects, Detection of Direction of Maximum Brightness and Color Detection. The result of image recognition is communicated to visually impaired people through pre-recorded verbal messages.…”

Section: Literature Reviewmentioning

confidence: 99%

Assistive Mobile Application for Visually Impaired People

Nayak

Chandrakala

2020

Int. J. Interact. Mob. Technol.

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According to the World Health Organization estimation, globally the number of people with some visual impairment is estimated to be 285 million, of whom 39 million are blind. The inability to use features such as sending and reading of email, schedule management, pathfinding or outdoor navigation, and reading SMS is a disadvantage for blind people in many professional and educational situations. Speech or text analysis can help improve support for visually-impaired people. Users can speak a command to perform a task. The spoken command will be interpreted by the Speech Recognition Engine (SRE) and can be converted into text or perform suitable actions. In this paper, an application that allows schedule management, emailing, and SMS reading completely based on voice command is proposed, implemented, and validated. The System hopes to provide blind people to simply speak the desired functionality and be guided thereby the system’s audio instructions. The proposed and designed app is implemented to support three languages which are English, Hindi, and Kannada.

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Section: Literature Reviewmentioning

confidence: 99%

Assistive Mobile Application for Visually Impaired People

Nayak

Chandrakala

2020

Int. J. Interact. Mob. Technol.

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“…All those sensors, however, increase the equipment requirements for such a solution to work, demanding a more specialized setup associated with more expensive hardware. Bagwan and Sankpal (2015) propose a solution called VisualPal that does not require environment adaptations, being able to recognize colors, brightness, and objects. VisualPal uses an arti cial neural network running on Android systems to recognize objects, however its performance is not discussed by the authors since processing time for object recognition is not reported.…”

Section: Related Workmentioning

confidence: 99%

“…As a consequence, our solution is still able to identify objects even if they are moved from one position to another in the environment. Similarly to other approaches based on object recognition (Deb et al, 2013, Bagwan and Sankpal, 2015, Matusiak et al, 2013, our solution uses SIFT descriptors, the already existing network infrastructure of an environment, 2) Client image sent to server; 3 and 4) Check similarity of client image against images in database; 5) Fetch metadata of database image that is most similar to client image; 6) Send metadata back to mobile client, which will provide the user with an audio feedback and a smartphone to provide a low-cost contextual guidance to visually impaired users. In that light, we clearly present the processing time and limitations regarding the use of SIFT descriptors in our approach, which is not clearly discussed by previous authors.…”

Section: Related Workmentioning

confidence: 99%

INSIDe: Image recognition tool aimed at helping visually impaired people contextualize indoor environments

Fank¹,

Bevilacqua²,

Duarte³

et al. 2019

RBCA

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Visually impaired (VI) people face a set of challenges when trying to orient and contextualize themselves. Computer vision and mobile devices can be valuable tools to help them improve their quality of life. This work presents a tool based on computer vision and image recognition to assist VI people to better contextualize themselves indoors. The tool works as follows: user takes a picture $\rho$ using a mobile application; ρ is sent to the server; ρ is compared to a database of previously taken pictures; server returns metadata of the database image that is most similar to ρ; finally the mobile application gives an audio feedback based on the received metadata. Similarity test among database images and $\rho$ is based on the search of nearest neighbors in key points extracted from the images by SIFT descriptors. Three experiments are presented to support the feasibility of the tool. We believe our solution is a low cost, convenient approach that can leverage existing IT infrastructure, e.g. wireless networks, and does not require any physical adaptation in the environment where it will be used.

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“…This can help visually impaired user in recognizing the objects in the surrounding. All the detections are provided to the visually impaired user with the help of voice output [2]. Fig.…”

Section: Visualpalmentioning

confidence: 99%

Assistive Technologies for Visually Impaired Users on Android Mobile Phones

A¹,

Chaudhari²

2018

IJCA

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With the increasing number of mobile phone users, mobile phones have become important means of communication and information sharing. Mobile phone users consist of sighted users and visually impaired users. Touchscreen mobile phones are inaccessible for visually impaired users due to the absence of any physical keys and insufficient accessibility features. An effective assistive technique for interacting with mobile phones may solve inaccessibility problem. This study describes various assistive applications currently available for different functionalities in mobile phones. These assistive applications provided accessibility for detecting surrounding and helping with direction. The study helps in understanding various techniques used. These techniques include Speech to Text converter, Text to Speech converter and various image recognition algorithms. The study and comparison of all these currently available applications and techniques is very important for proposing new application.

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VisualPal: A mobile app for object recognition for the visually impaired

Cited by 20 publications

References 5 publications

Assistive Mobile Application for Visually Impaired People

Assistive Mobile Application for Visually Impaired People

INSIDe: Image recognition tool aimed at helping visually impaired people contextualize indoor environments

Assistive Technologies for Visually Impaired Users on Android Mobile Phones

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