Adaptive histogram analysis for scene text binarization and recognition

Basavanna, M. and Shivakumara, P. and Srivatsa, S.K. and Hemantha Kumar, G. (2016) Adaptive histogram analysis for scene text binarization and recognition. Malaysian Journal of Computer Science, 29 (2). pp. 74-85. ISSN 0127-9084, DOI

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Scene text binarization and recognition is a challenging task due to different appearance of text in clutter background and uneven illumination in natural scene images. In this paper, we present a new method based on adaptive histogram analysis for each sliding window over a word of a text line detected by the text detection method. The histogram analysis works on the basis that intensity values of text pixels in each sliding window have uniform color. The method segments the words based on region growing which studies spacing between words and characters. Then we propose to use existing OCRs such as ABBYY and Tesseract (Google) to recognize the text line at word and character levels to validate the binarization results. The method is compared with well-known global thresholding technique of binarization to show its effectiveness.

Item Type: Article
Uncontrolled Keywords: Adaptive histogram analysis; Global thresholding; Region growing; Scene text binarization; Scene text recognition; Word segmentation
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 07 Dec 2017 02:56
Last Modified: 07 Dec 2017 02:56

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