Robustness Assessment of Texture Features for the Segmentation of Ancient Documents

Mehri, Maroua; Kieu, van Cuong; Mhiri, Mohamed; Héroux, Pierre; Gomez‐Krämer, Petra; Mahjoub, Mohamed Ali; Mullot, Rémy

doi:10.1109/das.2014.22

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…For instance, Cohen et al separated drawings from background and noise of ancient documents by using spatial and color features which were extracted from superpixels [20]. Mehri et al used an entropy rate superpixel segmentation algorithm based on a novel objective function [21] to obtain enhanced backgrounds of noisy ancient documents [22]. By setting the number of superpixels equal to 5% of image pixels, the entropy rate superpixel segmentation algorithm is carried out on ancient document image.…”

Section: Superpixel Techniquementioning

confidence: 99%

Use of SLIC superpixels for ancient document image enhancement and segmentation

et al. 2015

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Designing reliable and fast segmentation algorithms of ancient documents has been a topic of major interest for many libraries and the prime issue of research in the document analysis community. Thus, we propose in this article a fast ancient document enhancement and segmentation algorithm based on using Simple Linear Iterative Clustering (SLIC) superpixels and Gabor descriptors in a multi-scale approach. Firstly, in order to obtain enhanced backgrounds of noisy ancient documents, a novel foreground/background segmentation algorithm based on SLIC superpixels, is introduced. Once, the SLIC technique is carried out, the background and foreground superpixels are classified. Then, an enhanced and non-noisy background is achieved after processing the background superpixels. Subsequently, Gabor descriptors are only extracted from the selected foreground superpixels of the enhanced gray-level ancient book document images by adopting a multi-scale approach. Finally, for ancient document image segmentation, a foreground superpixel clustering task is performed by partitioning Gabor-based feature sets into compact and well-separated clusters in the feature space. The proposed algorithm does not assume any a priori information regarding document image content and structure and provides interesting results on a large corpus of ancient documents. Qualitative and numerical experiments are given to demonstrate the enhancement and segmentation quality.

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Section: Superpixel Techniquementioning

confidence: 99%

Use of SLIC superpixels for ancient document image enhancement and segmentation

et al. 2015

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“…In this article, we will focus only on texture-based features. The use of a texture-based approach in our work has been shown to be effective with skewed and degraded images [100]. We propose a framework which automatically extracts texture descriptors and involves a multiresolution/multiscale approach.…”

Section: Proposed Frameworkmentioning

confidence: 99%

A texture-based pixel labeling approach for historical books

Mehri

Gomez‐Krämer

Héroux

et al. 2015

Pattern Anal Applic

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Over the last few years, there has been tremendous growth in the automatic processing of digitized historical documents. In fact, finding reliable systems for the interpretation of ancient documents has been a topic of major interest for many libraries and the prime issue of research in the document analysis community. One important challenge is to refine well-known approaches based on strong a priori knowledge (e.g. the document image content, layout, typography, font size and type, scanning resolution, image size, etc.). Nevertheless, a texture analysis approach has consistently been chosen to segment a page layout when information is lacking on document structure and content. Thus, in this article a framework is proposed to investigate the use of texture as a tool for automatically determining homogeneous regions in a digitized historical book and segmenting its contents by extracting and analyzing texture features independently of the layout of the pages. The proposed framework is parameter-free and applicable to a large variety of ancient of books. It does not assume a priori information regarding document image content and structure. It consists of two phases: a texture-based feature extraction step and unsupervised clustering and labeling task based on the consensus clustering, hierarchical ascendant classification, and nearest neighbor search algorithms. The novelty of this work lies in the clustering of extracted texture descriptors to find automatically homogeneous regions, i.e. graphic and textual regions, by using the clustering approach on an entire book instead of processing each page individually. Our framework has been evaluated on a large variety of historical books and achieved promising results.

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“…-Digitization: Page skew, scanning defects (curvature, light, blur), presence of black borders. It has recently been shown that texture analysis plays a fundamental role for HDIA since it has been considered as a consistent choice for meeting the need to segment a page layout under significant degradation levels and different noise types [52]. Kise [42] also precised that the most relevant methods used to analyze pages with unconstrained layouts and overlapping layers are based on signal properties of page components by investigating texture-based features and techniques.…”

Section: Introductionmentioning

confidence: 99%

Texture feature benchmarking and evaluation for historical document image analysis

Mehri

Héroux

Gomez‐Krämer

et al. 2017

IJDAR

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The use of different texture-based methods is pervasive in different sub-fields and tasks of document image analysis and particularly in historical document image analysis. Nevertheless, faced with a large diversity of texturebased methods used for historical document image analysis, few questions arise. Which texture methods are firstly well suited for segmenting graphical contents from textual ones, discriminating various text fonts and scales, and separating different types of graphics? Then, which texture-based method represents a constructive compromise between the performance and the computational cost? Thus, in this article a benchmarking of the most classical and widely used texture-based feature sets has been conducted using a classical texture-based pixel-labeling scheme on a large corpus of historical documents to have satisfactory and clear answers to the above questions. We focus on determining the performance of each texture-based feature set according to the document content. The results reported in this study provide firstly a qualitative measure of which texture-based feature sets are the most appropriate, and secondly a useful benchmark in terms of performance and computational cost for current and future research efforts in historical document image analysis.

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Robustness Assessment of Texture Features for the Segmentation of Ancient Documents

Cited by 6 publications

References 14 publications

Use of SLIC superpixels for ancient document image enhancement and segmentation

Use of SLIC superpixels for ancient document image enhancement and segmentation

A texture-based pixel labeling approach for historical books

Texture feature benchmarking and evaluation for historical document image analysis

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