H-DIBCO 2010 - Handwritten Document Image Binarization Competition

Pratikakis, Ioannis; Gatos, Basilis; Ntirogiannis, Konstantinos

doi:10.1109/icfhr.2010.118

Cited by 174 publications

(114 citation statements)

References 7 publications

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“…Then, local and global methods are applied to the normalized images and the final results are obtained by combining these two complementary results. This method shows the state-of-the-art performance in the 2009 and 2010 Document Image Binarization Contests (DIBCOs) [5,24].…”

Section: Our Approachmentioning

confidence: 99%

Textline detection in degraded historical document images

Ahn

Ryu

Koo

et al. 2017

J Image Video Proc.

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This paper presents a textline detection method for degraded historical documents. Our method follows a conventional two-step procedure that the binarization is first performed and then the textlines are extracted from the binary image. In order to address the challenges in historical documents such as document degradation, structure noise, and skews, we develop new methods for the binarization and textline extraction. First, we improve the performance of binarization by detecting the non-text regions and processing only text regions. We also improve the textline detection method by extracting main textblock and compensating the skew angle and writing style. Experimental results show that the proposed method yields the state-of-the-art performance for several datasets.

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Section: Our Approachmentioning

confidence: 99%

Textline detection in degraded historical document images

Ahn

Ryu

Koo

et al. 2017

J Image Video Proc.

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“…These algorithms are commonly used as baseline for comparison with new algorithms, for example, in competitions, such as the Document Image Binarization Contest (DIBCO) held at the International Conference on Document Analysis and Recognition (ICDAR) [12,37,39] or the Competition on Handwritten Document Image Binarization (H-DIBCO) held at the International Conference on Frontiers in Handwriting Recognition (ICFHR) [30,36,38,40]. In the last two binarization competitions, in 2014 and 2016, the binarization algorithm by Howe [19] and derivations thereof using different preprocessing steps have won these contests.…”

Section: Image Binarizationmentioning

confidence: 99%

“…The document image binarization contest datasets from DIBCO 2009 [12], H-DIBCO 2010 [36], DIBCO 2011 [37], H-DIBCO 2012 [38], DIBCO 2013 [39], H-DIBCO 2014 [30] and H-DIBCO 2016 [40], were used in all experiments. These datasets contain 86 images in total, of which 65 images are handwritten and 21 images are printed documents.…”

Section: Experiments Setupmentioning

confidence: 99%

Efficient document image binarization using heterogeneous computing and parameter tuning

Westphal

Grahn

Lavesson

2018

IJDAR

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In the context of historical document analysis, image binarization is a first important step, which separates foreground from background, despite common image degradations, such as faded ink, stains, or bleed-through. Fast binarization has great significance when analyzing vast archives of document images, since even small inefficiencies can quickly accumulate to years of wasted execution time. Therefore, efficient binarization is especially relevant to companies and government institutions, who want to analyze their large collections of document images. The main challenge with this is to speed up the execution performance without affecting the binarization performance. We modify a state-of-the-art binarization algorithm and achieve on average a 3.5 times faster execution performance by correctly mapping this algorithm to a heterogeneous platform, consisting of a CPU and a GPU. Our proposed parameter tuning algorithm additionally improves the execution time for parameter tuning by a factor of 1.7, compared to previous parameter tuning algorithms. We see that for the chosen algorithm, machine learning-based parameter tuning improves the execution performance more than heterogeneous computing, when comparing absolute execution times.

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“…As illustrated in Figure 1(a), Historical documents are degraded due to bleed through. In addition, handwritten text documents are degraded due to a certain amount of variation in terms of the stroke width, stroke brightness, stroke connection, and document background as illustrated in Figure 1 [4] held under the framework of the International Conference on Frontiers in Handwritten Recognition show recent efforts on this issue. We participated in the DIBCO 2009 and our background estimation method [5] performs the best among entries of 43 algorithms submitted from 35 international research groups.…”

Section: Introductionmentioning

confidence: 99%