An adaptive over-split and merge algorithm for page segmentation

Dai-Ton, Ha; Nguyen, DucDung; Le, DucHieu

doi:10.1016/j.patrec.2016.06.011

Cited by 15 publications

(4 citation statements)

References 16 publications

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“…AOSM (Adaptive Over-Split and Merge algorithm) [21] is a hybrid page segmentation method combined top-down and bottom-up approaches. It firstly over-segments page image using a set of white-spaces covering the whole document background.…”

Section: The Aosm Methodsmentioning

confidence: 99%

ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

Antonacopoulos

Clausner

Papadopoulos

et al. 2015

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

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Section: The Aosm Methodsmentioning

confidence: 99%

ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

Antonacopoulos

Clausner

Papadopoulos

et al. 2015

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

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“…Hybrid methods combine the bottom-up and top-down techniques. The methods are effective for the segmentation of complex structure document [7], [14]. Connected components and delimiters (white space, tap stop) in a document page are extracted, filtered and analyzed.…”

Section: A Document Layout Analysismentioning

confidence: 99%

A Hybrid Method for Mathematical Expression Detection in Scientific Document Images

Phong

Hoang

2020

IEEE Access

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Mathematical expressions have been widely used in scientific documents. In order to analyze the documents, automatic detection of mathematical expressions is a crucial step. The paper presents a unified system for the detection of mathematical expressions including both inline and isolated expressions in scientific document images that usually consist of heterogeneous components (e.g., figures, tables, text and expressions). In the system, a hybrid method of two stages is proposed for the effective detection of mathematical expressions. First, the layout analysis of entire document images is introduced to improve the accuracy of text line and word segmentation. Then, both isolated and inline expressions in document images are detected. Both hand-crafted and deep learning features are extensively investigated and combined to improve the detection accuracy. Furthermore, a generic performance metric is applied to evaluate the system comprehensively. The proposed method has been evaluated on two public benchmark datasets (Marmot and GTDB). The obtained accuracies of isolated and inline expressions in the Marmot dataset are 91.18% and 81.35% while those in the GTDB dataset are 89.51% and 80.20%, respectively. The performance comparison is carried out with the conventional methods to show the outstanding effectiveness of the proposed system. Moreover, extensive experiments have been performed in order to point out the effect of document image resolution and post processing techniques on mathematical expression detection. INDEX TERMS Mathematical expression detection, document analysis, machine learning, neural network, fusion technique.

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“…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]. The second category tries to adapt to local variations in the document in order to be able to segment a broader range of layouts with the same algorithm [12]- [14].…”

Section: A Layout Segmentationmentioning

confidence: 99%

“…Precision and recall are estimated as follows, respectively. Precision = TP TP + FP (10) Recall = TP TP + FN (11) where True-Positive(TP), False-Positive(FP) and False-Negative(FN) with respect to text area, are defined as follows:…”

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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An adaptive over-split and merge algorithm for page segmentation

Cited by 15 publications

References 16 publications

ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

A Hybrid Method for Mathematical Expression Detection in Scientific Document Images

Segmentation and Recognition for Historical Tibetan Document Images

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