将手写笔迹转为极坐标令牌，用标准GPT生成逼真连笔字。
The Cursive Transformer
- 用极坐标离散化笔迹偏移，转为普通文本令牌序列训练
- 仅需3500个手写字词+简单增强即可生成真实连笔字
- 无需特殊结构，比传统RNN方法更简单高效
在分词文本、音频和图像上训练的Transformer可生成高质量自回归样本，但以笔迹坐标序列表示的手写数据仍研究不足。本文提出一种新分词方案：将笔画偏移转换为极坐标，离散化为桶，再转化为令牌序列，用于训练标准GPT模型。该方法无需专用架构（如混合密度网络或Graves 2014的自推进ASCII注意力头），即可捕捉复杂笔画分布。仅用3,500个手写字词和少量数据增强，便训练出能生成逼真连笔字的模型。该方法比以往RNN-based方法更简洁且性能更优。
原文摘要 · Abstract (English)
Transformers trained on tokenized text, audio, and images can generate high-quality autoregressive samples. But handwriting data, represented as sequences of pen coordinates, remains underexplored. We introduce a novel tokenization scheme that converts pen stroke offsets to polar coordinates, discretizes them into bins, and then turns them into sequences of tokens with which to train a standard GPT model. This allows us to capture complex stroke distributions without using any specialized architectures (eg. the mixture density network or the self-advancing ASCII attention head from Graves 2014). With just 3,500 handwritten words and a few simple data augmentations, we are able to train a model that can generate realistic cursive handwriting. Our approach is simpler and more performant than previous RNN-based methods.
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