计算机技术与发展2025，Vol.35Issue(5)：16-22,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0402
基于改进PSENet的西夏文检测研究
Research on Xixia Script Detection Based on Improved PSENet
摘要
Abstract
Due to the unique shape,complex structure,and numerous strokes of Xixia characters,as well as issues such as missing characters,discoloration,and fading in ancient Xixia texts,existing text detection models cannot accurately locate the characters.Therefore,we propose a Xixia character detection method based on an improved PSENet network model,building on a comprehensive analysis of current mainstream research.Firstly,the proposed method replaces the 3×3 convolution in the bottleneck of ResNet with PSA,forming EPSANet,which effectively extracts finer-grained multi-scale spatial information.Secondly,an Adaptive Attention Module(AAM)is introduced to reduce information loss during the feature map generation process.Finally,an Attention Feature Fusion module(AFF)is incorporated to better fuse features with inconsistent semantics and scales.Experimental results show that in the text detection task on the Xixia character dataset,the precision and F1-score of the improved model increased by 3.9 percentage points and 3.4 percentage points,respectively,compared to the standard PSENet model.Compared to other mainstream models,there are significant im-provements,demonstrating the effectiveness of the proposed method.关键词
文本检测/多尺度特征/特征融合/自适应注意力/西夏古籍Key words
text detection/multi-scale features/feature fusion/adaptive attention/Xixia ancient texts分类
计算机与自动化引用本文复制引用
于海庆,郑廷帅,史伟..基于改进PSENet的西夏文检测研究[J].计算机技术与发展,2025,35(5):16-22,7.基金项目
国家自然科学基金项目(62166030) （62166030）
