GL-GAN: Learning Both Global and Local Styles for Handwritten Text Generation with Mamba
Authors: Yiming Wang, Hongxi Wei, Heng Wang, ShiwenSun, Chao He


Reviewer paper summaries: The authors proposed a new architecture for a Handwritten text generation (HTG). They performed an ablation study to demonstrate that different building blocks of the architecture provide some additional improvements to HTG quality.
Summary of strengths: - an applied problem potentially useful for applications
- experimental results demonstrate some improvements in standard metrics such as NED, etc.
- ablation study for different parts of the architecture
Summary of weaknesses: - it is not clear to what extent diversity in the provided style samples (see figure 2) influence the final results
- sometimes demonstrated improvements are too small, e.g. see table 3, so it is not clear whether the differences are statistically significant
- although the authors provided some ablation study, they did not provide any justification/motivation of the proposed architecture design choices in sections 3.2-3.5
Comments, suggestions and typos: - I would propose to elaborate more on justification/motivation of the proposed design choices in sections 3.2-3.5
- I think that it could be good to make at least some small user study to evaluate quality of the generated HTG. I do not think that relying only on such metrics as FID, HWD and KID is enough, since these measures provide only some average statistical information about similarity of distributions
Overall assessment scores: 2.5