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Sublinear information bottleneck based two-stage deep learning approach to genealogy layout recognition.

Jianing You¹, Qing Wang¹

¹College of Information and Electrical Engineering, China Agricultural University, Beijing, China.

Frontiers in Neuroscience

|July 17, 2023

Summary

This study introduces a new genealogy layout recognition method using a sublinear information bottleneck (SIB) and deep learning. The approach accurately identifies and localizes genealogy components, improving research and preservation efforts.

Keywords:

ResNet YOLOv5 detector deep learning genealogy layout recognition sublinear information bottleneck

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Area of Science:

Computer Science
Digital Humanities
Cultural Heritage Informatics

Background:

Genealogy layout recognition is crucial for preserving human cultural heritage.
Existing methods struggle with the complexity and variability of genealogical data.
Automated recognition can significantly aid genealogy research and archival efforts.

Purpose of the Study:

To develop a novel and effective method for automated genealogy layout recognition.
To improve the accuracy and efficiency of identifying and localizing components within genealogy charts.
To enhance the preservation and accessibility of genealogical records.

Main Methods:

Introduction of a sublinear information bottleneck (SIB) for feature extraction from genealogy images.
Development of a two-stage deep learning approach utilizing SIB-ResNet (classifier) and SIB-YOLOv5 (object detector).
Evaluation on a dedicated dataset of genealogy images to assess performance.

Main Results:

The proposed SIB-based deep learning method achieved promising results on genealogy image recognition.
The approach demonstrated superior performance compared to existing state-of-the-art methods.
Accurate identification and localization of diverse genealogy layout components were achieved.

Conclusions:

The sublinear information bottleneck combined with deep learning offers a powerful solution for genealogy layout recognition.
This method has significant potential for applications in genealogy research, data extraction, and digital preservation.
The findings pave the way for more advanced automated analysis of historical and cultural documents.