Japanese handwriting generation improves with human preference feedback
Japanese Handwriting Generation Based on Human Preference Feedback Using Direct Preference Optimization
Computer Vision and Pattern Recognition
Summary
Generating Japanese handwriting that looks natural is hard because no two handwritings are exactly alike, even from the same person. The authors created a method that asks people which handwriting samples they like better to teach a computer model to generate more preferred handwriting. This approach doesn’t require huge amounts of handwriting data and still improves how accurate and natural the computer-generated handwriting looks. However, very complex styles like brush pen strokes remain a challenge for the method.
What this means in practice
- •For graphic designers: Generate personalized Japanese handwriting styles that better match client preferences without needing extensive handwriting data collection.
- •For handwriting recognition developers: Test handwriting recognition systems on a variety of realistic generated handwriting samples enhanced by human preference feedback.
Authors
Wakana Kuwata, Ryota Mibayashi, Masanori Tani, Hiroaki Ohshima
Abstract
We propose a Japanese handwriting generation framework that integrates human preference feedback using Direct Preference Optimization (DPO) to align generative models with human cognitive and stylistic preferences. While font generation technologies leveraging Generative Adversarial Networks and diffusion models have advanced significantly, applying these approaches to handwriting generation presents unique challenges. Font generation is a deterministic process yielding consistent styles. Handwriting generation, on the other hand, is inherently non-deterministic. In handwriting, glyph shapes never match exactly even when the same writer writes the same character. We define this shape difference as handwriting variability. Although this variability is essential for conveying the natural quality of human writing, it complicates model training and substantially increases data collection costs to capture diverse distribution patterns. To address these limitations, we incorporate human perceptual feedback into the training loop of handwriting generation. While collecting extensive handwritten data is costly, evaluating pairwise preferences between two generated samples remains very easy for humans. Applying DPO, an efficient RL-free preference optimization method, to our generation model enables high-quality handwriting generation without relying on large-scale dataset collection. Experimental results across four distinct Japanese handwriting styles demonstrate that DPO-based training enhances overall structural accuracy. However, in highly complex styles with significant variability, such as brush pen writing, the model fails to capture style-specific features. These findings highlight a critical challenge in extending DPO to complex handwriting styles.