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Tensor representation learning based image patch analysis for text identification and recognition.
- Source :
-
Pattern Recognition . Apr2015, Vol. 48 Issue 4, p1207-1220. 14p. - Publication Year :
- 2015
-
Abstract
- In this paper, we introduce a novel framework for text identification and recognition, called tensor representation learning based image patch analysis (TRL-IPA). Unlike most of previous text identification approaches, which can only be applied to binarized images, TRL-IPA can be directly applied to gray level and color images. TRL-IPA is built on a general formulation of the convergent tensor representation learning (CTRL) algorithms. In the implementation of TRL-IPA, image patches are represented in the form of tensors, while low dimensional representations of these tensors are learned via a CTRL algorithm. To identify text regions in new coming document images, a random forest classifier is trained in the learned tensor subspace. Moreover, the TRL-IPA framework can be straightforwardly applied to recognition problems, such as handwritten digits recognition. We conducted extensive experiments on ancient Chinese, Arabic and Cyrillic document images, to evaluate TRL-IPA on text identification tasks. Experimental results demonstrate its effectiveness and robustness. In addition, recognition results on images of handwritten digits show its advantage over state-of-the-art vector and tensor representation based approaches. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 48
- Issue :
- 4
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 100157075
- Full Text :
- https://doi.org/10.1016/j.patcog.2014.09.025