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Title: HMM-based multi oriented text recognition in natural scene image
Authors: Roy S.
Pratim Roy, Partha
Shivakumara P.
Louloudis G.
Tan C.L.
Pal U.
Published in: Proceedings of 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013
Abstract: 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. © 2013 IEEE.
Citation: Proceedings of 2nd IAPR Asian Conference on Pattern Recognition, ACPR 2013, (2013), 288- 292. Naha, Okinawa
Issue Date: 2013
Publisher: IEEE Computer Society
Keywords: Binarization
Curved Text Recognition
Hidden Markov Model
Scene Text Recognition
Author Scopus IDs: 57199785269
Author Affiliations: Roy, S., Tata Consultancy Services, Kolkata, India
Roy, P.P., CVPR Unit, Indian Statistical Institute, Kolkata, India
Shivakumara, P., University of Malaya, Kuala Lumpur, Malaysia
Louloudis, G., Computational Intelligence Laboratory, Demokritos, Greece
Tan, C.L., School of Computing, National University of Singapore, Singapore, Singapore
Pal, U., CVPR Unit, Indian Statistical Institute, Kolkata, India
Appears in Collections:Conference Publications [CS]

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