Proceedings ArticleDOI
New Binarization Approach Based on Text Block Extraction
Ines Ben Messaoud,Hamid Amiri,Haikal El Abed,Volker Märgner +3 more
- pp 1205-1209
TLDR
The aim of the present approach is the application of binarization algorithms on selected objects-of-interest on a combination between a preprocessing step and a localization step.Abstract:
Document analysis and recognition systems include, usually, several levels, annotation, preprocessing, segmentation, feature extraction, classification and post-processing. Each level may be dependent on or independent from the other levels. The presence of noise in images can affect the performance of the entire system. This noise can be introduced by the digitization step or from the document itself. In this paper, we present a new binarization approach based on a combination between a preprocessing step and a localization step. The aim of the present approach is the application of binarization algorithms on selected objects-of-interest. The evaluation of the developed approach is performed using two benchmarking datasets from the last two document binarization contests (DIBCO 2009 and H-DIBCO 2010). It shows very promising results.read more
Citations
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Proceedings ArticleDOI
ICDAR 2011 Document Image Binarization Contest (DIBCO 2011)
TL;DR: The contest details including the evaluation measures used as well as the performance of the 18 submitted methods are described along with a short description of each method.
Journal ArticleDOI
A combined approach for the binarization of handwritten document images
TL;DR: A combination of a global and a local adaptive binarization method at connected component level is proposed that aims in an improved overall performance and achieves top performance after extensive testing on the DIBCO (Document Image Binarization Contest) series datasets.
Journal ArticleDOI
Computer vision detection of surface defect on oranges by means of a sliding comparison window local segmentation algorithm
Dian Rong,Xiuqin Rao,Yibin Ying +2 more
TL;DR: The proposed image segmentation algorithm was able to correctly detect 97% of the defective orange and also presents the detailed image processing procedure including removal of background pixels, image binarization using local segmentation, image subtraction, image morphological modification, removal of stem end pixels for detecting surface defect in an orange gray-level image.
Book ChapterDOI
Historical document binarization based on phase information of images
TL;DR: In this paper, phase congruency features are used to develop a binarization method for degraded documents and manuscripts and Gaussian and median filtering are used in order to improve the final binarized output.
Journal ArticleDOI
GiB : A ${G}$ ame Theory ${I}$ nspired ${B}$ inarization Technique for Degraded Document Images
TL;DR: The experimental results show that GiB (Game theory Inspired Binarization) outperforms competing state-of-the-art methods in most cases.
References
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Journal ArticleDOI
A Computational Approach to Edge Detection
TL;DR: There is a natural uncertainty principle between detection and localization performance, which are the two main goals, and with this principle a single operator shape is derived which is optimal at any scale.
Journal ArticleDOI
Survey over image thresholding techniques and quantitative performance evaluation
Mehmet Sezgin,Bulent Sankur +1 more
TL;DR: 40 selected thresholding methods from various categories are compared in the context of nondestructive testing applications as well as for document images, and the thresholding algorithms that perform uniformly better over nonde- structive testing and document image applications are identified.
Journal ArticleDOI
Adaptive document image binarization
Jaakko Sauvola,Matti Pietikäinen +1 more
TL;DR: A new method is presented for adaptive document image binarization, where the page is considered as a collection of subcomponents such as text, background and picture, which adapts and performs well in each case qualitatively and quantitatively.