Geometrics Assisted Rubbing Generation and Semantics Enhanced Detection for Small and Dense OBI Character
Year: 2025
Type: article
Abstract: Character detection is essential for subsequent Oracle Bone Inscription (OBI) research. However, the lack of labeled data and the complexity of small and dense OBI characters are the main difficulties in OBI detection research. In this paper, we propose a framework for rubbing generation that can automatically build up largescale rubbing samples with verisimilar scenarios to noisy wild OBI through geometric and morphological construction combined with style transferring. Moreover, we propose a semantic-enhanced detection model aiming at small and dense OBI through the fusion of multi-resolution feature maps with the enriched feature in the YOLOv5s backbone. We introduce the higher resolution and the Soft-NMS into the proposed OBI detection model to solve the overlapping of small and dense OBI characters. The augmented dataset improves the performance of benchmark object detection models in the real OBI detection task when sufficient data is lacking. Furthermore, the proposed OBI detection model can provide easy and preferable access to OBI detection even with a small number of labeled data and obtain preferable results. Experiments ascertain the effectiveness of the proposed OBI generation framework and the proposed OBI detection model.
Authors: Xiuan Wan, Yuchun Fang, Jiahua Wu, Shouyong Pan
Institution: Shanghai University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4415469366
FWCI:
Cites:
Cited by:
Related to: