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Proceedings Paper

Segmenting texts from outdoor images taken by mobile phones using color features
Author(s): Zongyi Liu; Hanning Zhou
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Paper 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.

Paper Details

Date Published: 24 January 2011
PDF: 9 pages
Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740B (24 January 2011); doi: 10.1117/12.872149
Show Author Affiliations
Zongyi Liu, (United States)
Hanning Zhou, (United States)

Published in SPIE Proceedings Vol. 7874:
Document Recognition and Retrieval XVIII
Gady Agam; Christian Viard-Gaudin, Editor(s)

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