HMM-Based Multi Oriented Text Recognition in Natural Scene Image

Recognition of curved text in natural scene image is a challenging task. Due to complex background and unpredictable characteristics of scene text and noise, text characters in strings are often touching that affects the performance of segmentation and recognition. This paper presents a novel approa...

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Bibliographic Details
Published in:	Proceedings - IEEE Computer Society Conference on Pattern Recognition and Image Processing pp. 288 - 292
Main Authors:	Roy, Sangheeta, Roy, Partha Pratim, Shivakumara, Palaiahnakote, Louloudis, Georgios, Tan, Chew Lim, Pal, Umapada
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.11.2013
Subjects:	Binarization Character recognition Curved Text Recognition Feature extraction Hidden Markov Model Hidden Markov models Image recognition Image segmentation Optical character recognition software Scene Text Recognition Text recognition
ISSN:	0730-6512
Online Access:	Get full text
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Summary:	Recognition of curved text in natural scene image is a challenging task. Due to complex background and unpredictable characteristics of scene text and noise, text characters in strings are often touching that affects the performance of segmentation and recognition. This paper presents a novel approach for curved text recognition using Hidden Markov Models (HMM). From curved text, a path of sliding window is estimated and features extracted from the sliding window are fed to the HMM system for recognition. We evaluate two frame-wise feature extraction algorithms namely Marti-Bunk and local gradient histogram. The proposed approach has been tested on different natural scene benchmark as well as video databases, e.g. ICDAR-2003competition scene images, MSRA-TD500 and NUS. We have achieved word recognition accuracy of about 63.28%, 58.41% and 53.62%y for horizontal text, non-horizontal text and curved text, respectively.
ISSN:	0730-6512
DOI:	10.1109/ACPR.2013.60