A comprehensive survey of mostly textual document segmentation algorithms since 2008

Eskenazi, Sébastien; Gomez‐Krämer, Petra; Ogier, Jean-Marc

doi:10.1016/j.patcog.2016.10.023

Cited by 79 publications

(39 citation statements)

References 121 publications

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“…In the past decades, researchers have proposed some methods to extract layout information from the printed or handwritten document images. Sébastien et al [9] divided these methods into three categories. The first category is usually aiming at segmenting a specific, predefined kind of layout such as a Manhattan layout for instance [10], [11].…”

Section: A Layout Segmentationmentioning

confidence: 99%

Segmentation and Recognition for Historical Tibetan Document Images

Long

Duan

et al. 2020

IEEE Access

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As a shining pearl in traditional Tibetan culture, historical Tibetan documents have received extensive attention from historians, linguists and Buddhist scholars. These documents are converted into digital form using Tibetan document segmentation and recognition methods. The document digitization is of great significance for the research, protection and inheritance of Tibetan history. This paper proposes an overall segmentation and recognition framework for historical Tibetan document images. Firstly, the historical Tibetan document image is preprocessed to correct imbalanced illumination, tilt and noises, and is further transformed into the binarized image. Secondly, we propose a layout segmentation method based on block projection to segment Tibetan document images into texts, lines and frames. Thirdly, in order to solve the problems of touching strokes between text-lines and curvilinear text-lines, we present a text-line segmentation method based on graph model for historical Tibetan text-line segmentation. Lastly, we present a touching segmentation method to segment touching Tibetan character string, and then recognize Tibetan characters. Experimental results show our proposed methods on layout segmentation, text-line segmentation and touching character string segmentation, achieve the satisfactory performance. The proposed methods can also be applied to other fonts in Tibetan font family.

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Section: A Layout Segmentationmentioning

confidence: 99%

Segmentation and Recognition for Historical Tibetan Document Images

Long

Duan

et al. 2020

IEEE Access

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“…In order to develop a comprehensive model which can be used on diversified publishing styles, we chose ESWC 2016 challenge task 2 published dataset. Various gold standard datasets from ESWC challenge are available at the link 13 along with an evaluation tool. This dataset consists of research articles having diversified format and styles adopted from publishers like ACM, LNCS, and IEEE.…”

Section: A Datasetmentioning

confidence: 99%

FLAG-PDFe: Features Oriented Metadata Extraction Framework for Scientific Publications

Ahmed

Afzal

2020

IEEE Access

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The unprecedented growth of the research publications in diversified domains has overwhelmed the research community. It requires a cumbersome process to extract this enormous information by manually analyzing these research documents. To automatically extract content of a document in a structured way, metadata and content must be annotated. Scientific community has been focusing on automatic extraction of content by forming different heuristics and applying different machine learning techniques. One of the renowned conference organizers, ESWC organizes state-of-the-art challenge to extract metadata like authors, affiliations, countries in affiliations, supplementary material, sections, table, figures, funding agencies, and EU funded projects from PDF files of research articles. We have proposed a feature centric technique that can be used to extract logical layout structure of articles from publishers with diversified composition styles. To extract unique metadata from a research article placed in logical layout structure, we have developed a four-staged novel approach "FLAG-PDFe". The approach is built upon distinct and generic features based on the textual and the geometric information from the raw content of research documents. At the first stage, the distinct features are used to identify different physical layout components of an individual article. Since research journals follow their unique publishing styles and layout formats, therefore, we develop generic features to handle these diversified publishing patterns. We employ support vector classification (SVC) in the third stage to extract the logical layout structure (LLS)/ sections of an article, after performing comprehensive evaluation of generic features and machine learning models. Finally, we further apply heuristics on LLS to extract the desired metadata of an article. The outcomes of the study are obtained using the gold standard data set. The results yields 0.877 recall, precision 0.928 and 0.897 F-measure. Our approach has achieved a 16% gain on f-measure when compared to the best approach of the ESWC challenge.

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“…Eskenazi et al [9] surveyed and proposed a typology for most document segmentation algorithms between 2008 and 2017. According to this typology, PIVAJ's segmentation part would belong to group 3: Layout potentially unconstrained, subgroup hybrid techniques, as it combines methods of group 2's segmentation by feature classification (B&W for text and separators, grayscale for pictures) with another higher-level classifier for text level identification.…”

Section: Selection Of the Article Extraction Toolmentioning

confidence: 99%