Automatic evaluation of document binarization results
E. Badekas,Nikos Papamarkos +1 more
- pp 1005-1014
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TLDR
This paper proposes a new method which permits the estimation of the best parameter values for each one of the document binarization techniques and also the estimationOf the best document Binarization result of all techniques.Abstract:
Most of the document binarization techniques have many parameters that can initially be specified. Usually, subjective document binarization evaluation, employs human observes for the estimation of the best parameter values of the techniques. Thus, the selection of the best values for these parameters is crucial for the final binarization result. However, there is not any set of parameters that guarantees the best binarization result for all document images. It is important, the estimation of the best values to be adaptive for each one of the processing images. This paper proposes a new method which permits the estimation of the best parameter values for each one of the document binarization techniques and also the estimation of the best document binarization result of all techniques. In this way, document binarization techniques can be compared and evaluated using, for each one of them, the best parameter values for every document image.read more
Citations
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Proceedings ArticleDOI
Efficient implementation of local adaptive thresholding techniques using integral images
TL;DR: A fast adaptive binarization algorithm that yields the same quality of Binarization as the Sauvola method but runs in time close to that of global thresholding methods (like Otsu's method), independent of the window size.
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Performance Evaluation Methodology for Historical Document Image Binarization
TL;DR: This paper addresses a pixel-based binarization evaluation methodology for historical handwritten/machine-printed document images using a weighting scheme that diminishes any potential evaluation bias.
Proceedings ArticleDOI
Image Binarization for End-to-End Text Understanding in Natural Images
TL;DR: The main finding is the fact that image binarization methods combined with additional filtering of generated connected components and off-the-shelf OCR engines can achieve state-of- the-art performance for end-to-end text understanding in natural images.
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Efficient multiscale Sauvola's binarization
Guillaume Lazzara,Thierry Géraud +1 more
TL;DR: This implementation improves the robustness of Sauvola’s algorithm by making the results almost insensible to the window size whatever the object sizes, which makes it usable in full document analysis toolchains.
Journal ArticleDOI
Adaptive Binarization of Unconstrained Hand-Held Camera-Captured Document Images
TL;DR: This paper presents a new adaptive binarization technique for degraded hand-held camera-captured document images using the use of ridges detection for rough estimation of foreground regions in a document image and demonstrates that the method achieves better performance as compared to state-of-the-art global and localbinarization methods.
References
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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.
Book
On fuzzy algorithms
TL;DR: A fuzzy algorithm is introduced which, though fuzzy rather than precise in nature, may eventually prove to be of use in a wide variety of problems relating to information processing, control, pattern recognition, system identification, artificial intelligence and, more generally, decision processes involving incomplete or uncertain data.