University of Surrey

Test tubes in the lab Research in the ATI Dance Research

Text extraction from natural scene image: A survey

Zhang, H., Zhao, K., Song, Yi-Zhe and Guo, J. (2013) Text extraction from natural scene image: A survey Neurocomputing, 122. pp. 310-323.

Full text not available from this repository.

Abstract

With the increasing popularity of portable camera devices and embedded visual processing, text extraction from natural scene images has become a key problem that is deemed to change our everyday lives via novel applications such as augmented reality. Text extraction from natural scene images algorithms is generally composed of the following three stages: (i) detection and localization, (ii) text enhancement and segmentation and (iii) optical character recognition (OCR). The problem is challenging in nature due to variations in the font size and color, text alignment, illumination change and reflections. This paper aims to classify and assess the latest algorithms. More specifically, we draw attention to studies on the first two steps in the extraction process, since OCR is a well-studied area where powerful algorithms already exist. This paper offers to the researchers a link to public image database for the algorithm assessment of text extraction from natural scene images.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Zhang, H.
Zhao, K.
Song, Yi-Zhey.song@surrey.ac.uk
Guo, J.
Date : 25 December 2013
DOI : 10.1016/j.neucom.2013.05.037
Uncontrolled Keywords : Scene understanding; Text detection and localization; Text enhancement and segmentation; Text extraction
Depositing User : Clive Harris
Date Deposited : 30 Jul 2019 08:43
Last Modified : 30 Jul 2019 08:43
URI: http://epubs.surrey.ac.uk/id/eprint/852141

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year


Information about this web site

© The University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom.
+44 (0)1483 300800