Memory-efficient document layout analysis method using LD-net

Zhao, Haoyu; Min, Weidong; Wang, Qi; Wei, Zitai

doi:10.1007/s11042-022-12497-9

Cited by 4 publications

(1 citation statement)

References 32 publications

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“…Layout analysis [21,22,23] involves the automated analysis, recognition, and understanding of images, text, tables, and their spatial relationships within a document layout. Our research focuses on print publication layout recognition and utilization, examining this field from two distinct dimensions.…”

Section: Related Workmentioning

confidence: 99%

Intelligent electronic document layout recognition via deep learning

Zuo,

Du,

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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An novel intelligent electronic document layout recognition method via deep learning is proposed. A text detection approach is used to detect the string position along with region, and those adjacent regions are merged based on the distance between text zones, then the document layout style is determined by calculating the match degree between the printed document and the publication template set. The proposed recognition method constructs a electronic document representation tree, the location of the area bounding box is added to the tree. The maximum match distance between the trees is calculated, and is used for judging the document layout based on the structural similarity. Experimental results show that this method can quickly and accurately distinguish electronic document among different layout styles. Users can not only recognize the layout of this printed publication real time, but also find the desired layout style of the printed publication from a large number of printed publication images. The given method could meet different usage needs in practical applications.

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Section: Related Workmentioning

confidence: 99%