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Title: Recognizing text with perspective distortion in natural scenes
Authors: Phan, T.Q. 
Shivakumara, P.
Tian, S.
Tan, C.L. 
Issue Date: 2013
Citation: Phan, T.Q., Shivakumara, P., Tian, S., Tan, C.L. (2013). Recognizing text with perspective distortion in natural scenes. Proceedings of the IEEE International Conference on Computer Vision : 569-576. ScholarBank@NUS Repository.
Abstract: This paper presents an approach to text recognition in natural scene images. Unlike most existing works which assume that texts are horizontal and frontal parallel to the image plane, our method is able to recognize perspective texts of arbitrary orientations. For individual character recognition, we adopt a bag-of-key points approach, in which Scale Invariant Feature Transform (SIFT) descriptors are extracted densely and quantized using a pre-trained vocabulary. Following [1, 2], the context information is utilized through lexicons. We formulate word recognition as finding the optimal alignment between the set of characters and the list of lexicon words. Furthermore, we introduce a new dataset called StreetViewText-Perspective, which contains texts in street images with a great variety of viewpoints. Experimental results on public datasets and the proposed dataset show that our method significantly outperforms the state-of-the-art on perspective texts of arbitrary orientations. © 2013 IEEE.
Source Title: Proceedings of the IEEE International Conference on Computer Vision
ISBN: 9781479928392
DOI: 10.1109/ICCV.2013.76
Appears in Collections:Staff Publications

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