WriteViT: Handwritten Text Generation with Vision Transformer

• URL: http://arxiv.org/abs/2505.13235v1
• Date: Mon, 19 May 2025 15:17:53 GMT
• Title: WriteViT: Handwritten Text Generation with Vision Transformer
• Authors: Dang Hoai Nam, Huynh Tong Dang Khoa, Vo Nguyen Le Duy,
• Abstract summary: We introduce WriteViT, a one-shot handwritten text synthesis framework that incorporates Vision Transformers (ViT)<n>WriteViT produces high-quality, style-consistent handwriting while maintaining strong recognition performance in low-resource scenarios.<n>These results highlight the promise of transformer-based designs for multilingual handwriting generation and efficient style adaptation.
• Score: 7.10052009802944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans can quickly generalize handwriting styles from a single example by intuitively separating content from style. Machines, however, struggle with this task, especially in low-data settings, often missing subtle spatial and stylistic cues. Motivated by this gap, we introduce WriteViT, a one-shot handwritten text synthesis framework that incorporates Vision Transformers (ViT), a family of models that have shown strong performance across various computer vision tasks. WriteViT integrates a ViT-based Writer Identifier for extracting style embeddings, a multi-scale generator built with Transformer encoder-decoder blocks enhanced by conditional positional encoding (CPE), and a lightweight ViT-based recognizer. While previous methods typically rely on CNNs or CRNNs, our design leverages transformers in key components to better capture both fine-grained stroke details and higher-level style information. Although handwritten text synthesis has been widely explored, its application to Vietnamese -- a language rich in diacritics and complex typography -- remains limited. Experiments on Vietnamese and English datasets demonstrate that WriteViT produces high-quality, style-consistent handwriting while maintaining strong recognition performance in low-resource scenarios. These results highlight the promise of transformer-based designs for multilingual handwriting generation and efficient style adaptation.

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