Segmenting texts from outdoor images taken by mobile phones using color features

Zongyi Liu; Hanning Zhou

doi:10.1117/12.872149

24 January 2011 Segmenting texts from outdoor images taken by mobile phones using color features

Zongyi Liu, Hanning Zhou

Proceedings Volume 7874, Document Recognition and Retrieval XVIII; 78740B (2011) https://doi.org/10.1117/12.872149
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Recognizing texts from images taken by mobile phones with low resolution has wide applications. It has been shown that a good image binarization can substantially improve the performances of OCR engines. In this paper, we present a framework to segment texts from outdoor images taken by mobile phones using color features. The framework consists of three steps: (i) the initial process including image enhancement, binarization and noise filtering, where we binarize the input images in each RGB channel, and apply component level noise filtering; (ii) grouping components into blocks using color features, where we compute the component similarities by dynamically adjusting the weights of RGB channels, and merge groups hierachically, and (iii) blocks selection, where we use the run-length features and choose the Support Vector Machine (SVM) as the classifier. We tested the algorithm using 13 outdoor images taken by an old-style LG-64693 mobile phone with 640x480 resolution. We compared the segmentation results with Tsar's algorithm, a state-of-the-art camera text detection algorithm, and show that our algorithm is more robust, particularly in terms of the false alarm rates. In addition, we also evaluated the impacts of our algorithm on the Abbyy's FineReader, one of the most popular commercial OCR engines in the market.

Citation Download Citation

Zongyi Liu and Hanning Zhou "Segmenting texts from outdoor images taken by mobile phones using color features", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740B (24 January 2011); https://doi.org/10.1117/12.872149

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available