@article {10.3844/jcssp.2013.1793.1802,
article_type = {journal},
title = {TEXT SIGNAGE RECOGNITION IN ANDROID MOBILE DEVICES},
author = {Foong, Oi-Mean and Sulaiman, Suziah and Ling, Kiing Kiu},
volume = {9},
number = {12},
year = {2013},
month = {Nov},
pages = {1793-1802},
doi = {10.3844/jcssp.2013.1793.1802},
url = {https://thescipub.com/abstract/jcssp.2013.1793.1802},
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.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}