Text segmentation and recognition in complex background based on Markov random field
Datong Chen,J.-M. Olobez,Hervé Bourlard +2 more
- 11 Aug 2002
- Vol. 4, pp 40227
TL;DR: By varying the number of gaussians, multiple hypotheses are provided to an OCR system and the final result is selected from the set of outputs, leading to an improvement of the system's performances.
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Abstract: In this paper we propose a method to segment and recognize text embedded in video and images. We modelize the gray level distribution in the text images as mixture of gaussians, and then assign each pixel to one of the gaussian layer. The assignment is based on prior of the contextual information, which is modeled by a Markov random field (MRF) with online estimated coefficients. Each layer is then processed through a connected component analysis module and forwarded to the OCR system as one segmentation hypothesis. By varying the number of gaussians, multiple hypotheses are provided to an OCR system and the final result is selected from the set of outputs, leading to an improvement of the system's performances.
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Citations
Scene Text Recognition using Higher Order Language Priors
Anand Mishra,Karteek Alahari,C. V. Jawahar +2 more
- 07 Sep 2009
TL;DR: A framework is presented that uses a higher order prior computed from an English dictionary to recognize a word, which may or may not be a part of the dictionary, and achieves significant improvement in word recognition accuracies without using a restricted word list.
Automatic text segmentation from complex background
Qixiang Ye,Wen Gao,Qingmig Huang +2 more
- 24 Oct 2004
TL;DR: An automatic method to segment text from complex background for recognition task by using a rule-based sampling method and trained GMMs together with the spatial connectivity information.
51
End-to-end scene text recognition using tree-structured models
TL;DR: A robust end-to-end scene text recognition method, which utilizes tree-structured character models and normalized pictorial structured word models, which outperforms state-of-the-art methods both for text localization and word recognition.
50
Scene Text Recognition Using Structure-Guided Character Detection and Linguistic Knowledge
TL;DR: A novel scene text-recognition method integrating structure-guided character detection and linguistic knowledge is proposed, using part-based tree structure to model each category of characters so as to detect and recognize characters simultaneously.
41
A Robust Split-and-Merge Text Segmentation Approach for Images
Yaowen Zhan,Weiqiang Wang,Wen Gao +2 more
- 20 Aug 2006
TL;DR: The proposed approach has a good performance in character recognition rate and processing speed, and is robust to text color, font size, as well as different styles of characters in different languages.
33
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Text identification in complex background using SVM
Datong Chen,Hervé Bourlard,Jean-Philippe Thiran +2 more
- 01 Dec 2001
TL;DR: A fast and robust algorithm to identify text in image or video frames with complex backgrounds and compression effects with advantages compared to conventional methods in both identification quality and computation time is presented.
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