eprintid: 3244
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/32/44
datestamp: 2023-11-09 15:51:30
lastmod: 2023-11-09 15:51:30
status_changed: 2023-11-09 15:46:24
type: article
metadata_visibility: show
creators_name: Foong, O.-M.
creators_name: Sulaiman, S.
creators_name: Ling, K.K.
title: Text signage recognition in android mobile devices
ispublished: pub
note: cited By 8
abstract: This study presents a Text Signage Recognition (TSR) model in Android mobile devices for Visually Impaired People (VIP). Independence navigation is always a challenge to VIP for indoor navigation in unfamiliar surroundings. Assistive Technology such as Android smart devices has great potential to assist VIPs in indoor navigation using built-in speech synthesizer. In contrast to previous TSR research which was deployed in standalone personal computer system using Otsu's algorithm, we have developed an affordable Text Signage Recognition in Android Mobile Devices using Tesseract OCR engine. The proposed TSR model used the input images from the International Conference on Document Analysis and Recognition (ICDAR) 2003 dataset for system training and testing. The TSR model was tested by four volunteers who were blind-folded. The system performance of the TSR model was assessed using different metrics (i.e., Precision, Recall, F-Score and Recognition Formulas) to determine its accuracy. =Experimental results show that the proposed TSR model has achieved recognition rate satisfactorily. © 2013 Science Publications.
date: 2013
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890355512&doi=10.3844%2fjcssp.2013.1793.1802&partnerID=40&md5=e29359cbb740470a4376f76261e011fa
id_number: 10.3844/jcssp.2013.1793.1802
full_text_status: none
publication: Journal of Computer Science
volume: 9
number: 12
pagerange: 1793-1802
refereed: TRUE
issn: 15493636
citation:   Foong, O.-M. and Sulaiman, S. and Ling, K.K.  (2013) Text signage recognition in android mobile devices.  Journal of Computer Science, 9 (12).  pp. 1793-1802.  ISSN 15493636