Research on automatic segmentation and recognition of single characters of original topography in intelligent recognition of oracle bone inscriptions

Minghang Lv; Siqi Bo; Fuli Li; Pengjie Wu; Zicheng Xiong

doi:10.1117/12.3041507

10 September 2024 Research on automatic segmentation and recognition of single characters of original topography in intelligent recognition of oracle bone inscriptions

Minghang Lv, Siqi Bo, Fuli Li, Pengjie Wu, Zicheng Xiong

Author Affiliations +

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 132570F (2024) https://doi.org/10.1117/12.3041507
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

Intelligent recognition technology, as an important technical tool, has played a significant role in many fields. In this paper, for the issue of oracle bone image processing, we first conducted image pre-processing, including size adjustment, normalization, and data enhancement steps, to improve image quality and highlight the oracle bone information. Then, the YOLOv5 model is utilized for oracle bone image segmentation, achieving high recognition accuracy after 50 epochs of training. Finally, the model was employed to automatically segment the original oracle bone topography images for individual characters, with an average processing time of only 810.8ms, demonstrating efficient processing speed. In total, 1447 oracle bones were automatically detected and segmented, achieving a high level of accuracy.

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

Citation Download Citation

Minghang Lv, Siqi Bo, Fuli Li, Pengjie Wu, and Zicheng Xiong "Research on automatic segmentation and recognition of single characters of original topography in intelligent recognition of oracle bone inscriptions", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 132570F (10 September 2024); https://doi.org/10.1117/12.3041507

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KEYWORDS

Bone

Data modeling

Image segmentation

Performance modeling

Education and training

Image processing

Visual process modeling

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