Parameter tuning for document image binarization using a racing algorithm

Mesquita, Rafael; Silva, Ricardo M. A.; Mello, Carlos A. B.; Miranda, Péricles

doi:10.1016/j.eswa.2014.10.039

Cited by 23 publications

(8 citation statements)

References 32 publications

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“…Nina et al [108] use a fixed 21 × 21 window size, while Mitianoudis and Papamarkos [92] progressively grow the window size at each pixel until the local standard deviation stabilizes. Morphological grayscale closing [24,70,85,86] and heuristic estimation [156] have also been used.…”

Section: Background Estimationmentioning

confidence: 99%

Historical Document Image Binarization: A Review

Tensmeyer

Martinez

2020

SN COMPUT. SCI.

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This review provides a comprehensive view of the field of historical document image binarization with a focus on the contributions made in the last decade. After the introduction of a standard benchmark dataset with the 2009 Document Image Binarization Contest, research in the field accelerated. Besides the standard methods for image thresholding, preprocessing, and post-processing, we review the literature on methods such as statistical models, pixel classification with learning algorithms, and parameter tuning. In addition to reviewing binarization algorithms, we discuss the available public datasets and evaluation metrics, including those that require pixel-level ground truth and those that do not. We conclude with recommendations for future work.

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Section: Background Estimationmentioning

confidence: 99%

Historical Document Image Binarization: A Review

Tensmeyer

Martinez

2020

SN COMPUT. SCI.

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“…The exact minimization of the energy function can be obtained by solving the equivalent Max Flow Problem [7]. A variant of [7] is proposed in [8] where the authors use statistical racing procedure, named I/F-Race, to automatically tune the parameters for two binarization algorithms reasoned on the perception of objects by distance (POD) and POD combined with a Laplacian energy based technique. By combining the local image contrast and the local image gradients to determine edges between text and background, a method is proposed by Su et al in [9] which won the DIBCO-13 competition.…”

Section: Related Workmentioning

confidence: 99%

Learning Free Document Image Binarization Based on Fast Fuzzy C-Means Clustering

Mondal

Coustaty

Gomez‐Krämer

et al. 2019

2019 International Conference on Document Analysis and Recognition (ICDAR)

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In this paper, a novel local threshold binarization method using fast Fuzzy C-Means clustering is proposed. Historical document images with non-uniform background, stains, faded ink are first processed by removing the background using inpainting based method. Then using Fuzzy C-Means clustering is used to cluster out the pixels into three main clusters : sure text pixels, sure background pixels and confused pixels which may or may not be labeled as text. Based on the structural symmetry of pixels (SSP), these confused pixels are then classified into text or background pixels. The SSP is defined as those pixels around strokes whose gradient magnitudes are big enough and whose directions are opposite. As the gradient map is our basis for computing the SSP, we further propose to estimate the background surface first and to extract potential SSP in the compensated image so as to deal with degradations of document images such as uneven illumination, low contrast and stain. To prove the effectiveness of our method, tests on eight public document image datasets are preformed and the experimental results show that our method outperforms other local threshold binarization approaches on both F-measure and PSNR.

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“…In [30] the authors introduced a new a document image binarisation method, in which they investigated the application of a racing procedure based on a statistical approach, named I/F‐Race, to suggest the parameters of the proposed technique. The first step of the proposed technique is the perception of objects by distance (POD).…”

Section: Related Workmentioning

confidence: 99%