Intelligent electronic document layout recognition via deep learning

Shiming Zuo; Zhenlong Du; Xiaoli Li; Tao Zhou

doi:10.1117/12.3021604

25 March 2024 Intelligent electronic document layout recognition via deep learning

Shiming Zuo, Zhenlong Du, Xiaoli Li, Tao Zhou

Proceedings Volume 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023); 130891E (2024) https://doi.org/10.1117/12.3021604
Event: Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 2023, Suzhou, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shiming Zuo, Zhenlong Du, Xiaoli Li, and Tao Zhou "Intelligent electronic document layout recognition via deep learning", Proc. SPIE 13089, Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023), 130891E (25 March 2024); https://doi.org/10.1117/12.3021604

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