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Proceedings Paper

Text recognition and correction for automated data collection by mobile devices
Author(s): Suleyman Ozarslan; P. Erhan Eren
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Paper Abstract

Participatory sensing is an approach which allows mobile devices such as mobile phones to be used for data collection, analysis and sharing processes by individuals. Data collection is the first and most important part of a participatory sensing system, but it is time consuming for the participants. In this paper, we discuss automatic data collection approaches for reducing the time required for collection, and increasing the amount of collected data. In this context, we explore automated text recognition on images of store receipts which are captured by mobile phone cameras, and the correction of the recognized text. Accordingly, our first goal is to evaluate the performance of the Optical Character Recognition (OCR) method with respect to data collection from store receipt images. Images captured by mobile phones exhibit some typical problems, and common image processing methods cannot handle some of them. Consequently, the second goal is to address these types of problems through our proposed Knowledge Based Correction (KBC) method used in support of the OCR, and also to evaluate the KBC method with respect to the improvement on the accurate recognition rate. Results of the experiments show that the KBC method improves the accurate data recognition rate noticeably.

Paper Details

Date Published: 3 March 2014
PDF: 6 pages
Proc. SPIE 9027, Imaging and Multimedia Analytics in a Web and Mobile World 2014, 902706 (3 March 2014); doi: 10.1117/12.2040668
Show Author Affiliations
Suleyman Ozarslan, Middle East Technical Univ. (Turkey)
P. Erhan Eren, Middle East Technical Univ. (Turkey)

Published in SPIE Proceedings Vol. 9027:
Imaging and Multimedia Analytics in a Web and Mobile World 2014
Qian Lin; Jan Philip Allebach; Zhigang Fan, Editor(s)

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