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Recognition of Bookmark Aging Degree Based on Probabilistic Neural Network
Cong Zheng1, Xiaoling Zhang1, Shaoqiu Ma1
1The School of Mathematical Engineering, Zhejiang Dongfang Polytechnic, Wenzhou 325000, China.
Computational Intelligence and Neuroscience
|February 28, 2022
Summary
This study introduces an automated method for detecting bookmark aging using image processing and neural networks. The technique accurately identifies aging defects, improving efficiency for librarians and borrowers.
Area of Science:
- Library Science
- Computer Vision
- Artificial Intelligence
Background:
- Manual bookmark aging checks are time-consuming and labor-intensive.
- Efficient bookmark management is crucial for library operations and user experience.
Purpose of the Study:
- To develop an automated system for recognizing bookmark aging defects.
- To improve the efficiency and accuracy of bookmark condition assessment.
Main Methods:
- Image preprocessing techniques applied to bookmark images.
- Otsu threshold segmentation and morphological processing for feature extraction.
- A trained neural network for defect recognition based on acquired aging features.
Main Results:
- The proposed image processing method achieved a 96% recognition rate for bookmark aging.
- Demonstrated accurate identification of aging defects in bookmarks.
- Successfully automated the process of checking bookmark condition.
Conclusions:
- The developed image processing and neural network approach offers an effective solution for automated bookmark aging recognition.
- This method significantly enhances the accuracy and efficiency of assessing bookmark condition compared to manual methods.
