A Multi-Scale Hybrid Attention Network for Sentence Segmentation Line Detection in Dongba Scripture

Junyao Xing, Xiaojun Bi () and Yu Weng
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Junyao Xing: College of Information and Communication Engineering, Harbin Engineering University, Harbin 150009, China
Xiaojun Bi: School of Information and Engineerin, Minzu University of China, Beijing 100081, China
Yu Weng: School of Information and Engineerin, Minzu University of China, Beijing 100081, China

Mathematics, 2023, vol. 11, issue 15, 1-18

Abstract: Dongba scripture sentence segmentation is an important and basic work in the digitization and machine translation of Dongba scripture. Dongba scripture sentence segmentation line detection (DS-SSLD) as a core technology of Dongba scripture sentence segmentation is a challenging task due to its own distinctiveness, such as high inherent noise interference and nonstandard sentence segmentation lines. Recently, projection-based methods have been adopted. However, these methods are difficult when dealing with the following two problems. The first is the noisy problem, where a large number of noise in the Dongba scripture image interference detection results. The second is the Dongba scripture inherent characteristics, where many vertical lines in Dongba hieroglyphs are easily confused with the vertical sentence segmentation lines. Therefore, this paper aims to propose a module based on the convolutional neural network (CNN) to improve the accuracy of DS-SSLD. To achieve this, we first construct a tagged dataset for training and testing DS-SSLD, including 2504 real images collected from Dongba scripture books and sentence segmentation targets. Then, we propose a multi-scale hybrid attention network (Multi-HAN) based on YOLOv5s, where a multiple hybrid attention unit (MHAU) is used to enhance the distinction between important features and redundant features, and the multi-scale cross-stage partial unit (Multi-CSPU) is used to realize multi-scale and richer feature representation. The experiment is carried out on the Dongba scripture sentence segmentation dataset we built. The experimental results show that the proposed method exhibits excellent detection performance and outperforms several state-of-the-art methods.

Keywords: hybrid attention mechanism; multi-scale depthwise convolution; multi-scale features; sentence segmentation line detection; Dongba scripture sentence segmentation line detection dataset (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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