PSA-HWT:基于金字塔挤压注意力的手写字体生成技术

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Hong Zhao, Jinhai Huang, Wengai Li, Zhaobin Chang, Weijie Wang
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引用次数: 0

摘要

该生成器以卷积神经网络(CNN)和变换器为核心模块,可作为手写字体生成网络的主要模型,并显示出有效的性能。然而,在字体的整体结构、笔画粗细和笔画弧度等方面仍存在特征提取不足的问题,导致生成的字体细节不够丰富。为了解决这些问题,我们提出了一种基于金字塔挤压注意力的手写字体生成模型的构建方法,称为 PSA-HWT。PSA-HWT 模型分为两部分:编码器和解码器。在编码器中,使用多分支结构从输入特征图中提取不同尺度的空间信息,实现多尺度特征提取。这有助于更好地捕捉字体的语义信息和全局结构,帮助生成模型理解字体的形状、粗细和弧度等细粒度特征。在解码器中,它使用自我关注机制来捕捉输入序列中不同位置的依赖关系。这有助于更好地理解生成的笔画或字符与正在生成的手写字体之间的关系,确保生成的手写文本的整体一致性。在 IAM 数据集上的实验结果表明,与目前的先进方法相比,PSA-HWT 的弗雷谢特起始距离 (FID) 分数降低了 16.35%,几何分数 (GS) 降低了 13.09%。这表明 PSA-HWT 生成的手写字体质量更高,更有实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PSA-HWT: handwritten font generation based on pyramid squeeze attention
The generator, which combines convolutional neural network (CNN) and Transformer as its core modules, serves as the primary model for the handwriting font generation network and demonstrates effective performance. However, there are still problems with insufficient feature extraction in the overall structure of the font, the thickness of strokes, and the curvature of strokes, resulting in subpar detail in the generated fonts. To solve the problems, we propose a method for constructing a handwritten font generation model based on Pyramid Squeeze Attention, called PSA-HWT. The PSA-HWT model is divided into two parts: an encoder and a decoder. In the encoder, a multi-branch structure is used to extract spatial information at different scales from the input feature map, achieving multi-scale feature extraction. This helps better capture the semantic information and global structure of the font, aiding the generation model in understanding fine-grained features such as the shape, thickness, and curvature of the font. In the decoder, it uses a self-attention mechanism to capture dependencies across various positions in the input sequence. This helps to better understand the relationship between the generated strokes or characters and the handwritten font being generated, ensuring the overall coherence of the generated handwritten text. The experimental results on the IAM dataset demonstrate that PSA-HWT achieves a 16.35% decrease in Fréchet inception distance (FID) score and a 13.09% decrease in Geometry Score (GS) compared to the current advanced methods. This indicates that PSA-HWT generates handwritten fonts of higher quality, making it more practically valuable.
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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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