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Recognizing Elevator Buttons and Labels for Blind Navigation

by Jingya Liu, Yingli Tian
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 35
Year of Publication: 2020
Authors: Jingya Liu, Yingli Tian
10.5120/ijca2020919835

Jingya Liu, Yingli Tian . Recognizing Elevator Buttons and Labels for Blind Navigation. International Journal of Computer Applications. 177, 35 ( Feb 2020), 1-8. DOI=10.5120/ijca2020919835

@article{ 10.5120/ijca2020919835,
author = { Jingya Liu, Yingli Tian },
title = { Recognizing Elevator Buttons and Labels for Blind Navigation },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2020 },
volume = { 177 },
number = { 35 },
month = { Feb },
year = { 2020 },
issn = { 0975-8887 },
pages = { 1-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number35/31128-2020919835/ },
doi = { 10.5120/ijca2020919835 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:47:45.341572+05:30
%A Jingya Liu
%A Yingli Tian
%T Recognizing Elevator Buttons and Labels for Blind Navigation
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 35
%P 1-8
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a cascade framework is proposed to detect elevator buttons and recognize their labels from images for blind navigation. First, a pixel-level mask of elevator buttons is segmented based on deep neural networks. Then a fast scene text detector is applied to recognize the text labels in the image as well as to extract their spatial vectors. Finally, all the detected buttons and their associated labels are paired by combining the button mask and spatial vectors of labels based on their location distribution. The cascade framework is conducive to multitask but the accuracy may decrease task by task. To avoid the limitation of the intermediate task, a new schema is further introduced by pairing buttons with their labels to consider the region of button and label as a whole. First, the regions of button-label pairs are detected and then the label for each pair is recognized. To evaluate the proposed method, an elevator button detection dataset is collected including 1,000 images containing buttons captured from both inside and outside of elevators with annotations of button locations and labels and 500 images are captured in elevators but without button buttons which are used for negative images in the experiments. Preliminary results demonstrate the robustness and effectiveness of the proposed method for elevator button detection and associated label recognition.

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Index Terms

Computer Science
Information Sciences

Keywords

Object detection Semantic segmentation Computer vision Deep learning

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