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PSA-HWT: handwritten font generation based on pyramid squeeze attention

  • 0School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China.
Peerj. Computer Science +

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December 16, 2024

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December 16, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces PSA-HWT, a novel handwritten font generation model. PSA-HWT significantly improves font quality by enhancing feature extraction and ensuring coherence, outperforming existing methods.

Area Of Science

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background

  • Existing handwriting font generation models, often based on Convolutional Neural Networks (CNNs) and Transformers, struggle with detailed feature extraction, leading to suboptimal font quality.
  • Specific issues include insufficient extraction of overall font structure, stroke thickness, and stroke curvature, limiting the realism and detail of generated fonts.

Purpose Of The Study

  • To propose a novel handwritten font generation model, PSA-HWT, that addresses the limitations of current methods by incorporating Pyramid Squeeze Attention (PSA).
  • To enhance the extraction of multi-scale spatial information and improve the understanding of fine-grained font features like shape, thickness, and curvature.
  • To ensure the coherence and quality of generated handwritten text through an effective encoder-decoder architecture with self-attention mechanisms.

Main Methods

  • The proposed PSA-HWT model utilizes a two-part encoder-decoder architecture.
  • The encoder employs a multi-branch structure for multi-scale feature extraction, capturing semantic information and global font structure.
  • The decoder incorporates a self-attention mechanism to model dependencies within the generated sequence, ensuring stroke and character coherence.

Main Results

  • Experiments on the IAM dataset show PSA-HWT achieves a 16.35% reduction in Fréchet Inception Distance (FID) score.
  • The model also demonstrates a 13.09% decrease in Geometry Score (GS) compared to state-of-the-art methods.
  • These results indicate a significant improvement in the quality and detail of generated handwritten fonts.

Conclusions

  • The PSA-HWT model effectively addresses limitations in feature extraction for handwritten font generation.
  • The integration of Pyramid Squeeze Attention and a self-attention mechanism leads to superior font quality and practical value.
  • PSA-HWT represents a significant advancement in generating realistic and high-fidelity handwritten fonts.

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