Layout Stroke Imitation: A Layout Guided Handwriting Stroke Generation for Style Imitation with Diffusion Model

Handwriting stroke generation is crucial for improving the performance of tasks such as handwriting recognition and writers order recovery. In handwriting stroke generation, it is significantly important to imitate the sample calligraphic style. The previous studies have suggested utilizing the calligraphic features of the handwriting. However, they had not considered word spacing (word layout) as an explicit handwriting feature, which results in inconsistent word spacing for style imitation. Firstly, this work proposes multi-scale attention features for calligraphic style imitation. These multi-scale feature embeddings highlight the local and global style features. Secondly, we propose to include the words layout, which facilitates word spacing for handwriting stroke generation. Moreover, we propose a conditional diffusion model to predict strokes in contrast to previous work, which directly generated style images. Stroke generation provides additional temporal coordinate information, which is lacking in image generation. Hence, our proposed conditional diffusion model for stroke generation is guided by calligraphic style and word layout for better handwriting imitation and stroke generation in a calligraphic style. Our experimentation shows that the proposed diffusion model outperforms the current state-of-the-art stroke generation and is competitive with recent image generation networks.

Weakly Supervised Annotations for Multi-modal Greeting Cards Dataset

In recent years, there is a growing number of pre-trained models trained on a large corpus of data and yielding good performance on various tasks such as classifying multimodal datasets. These models have shown good performance on natural images but are not fully explored for scarce abstract concepts in images. In this work, we introduce an image/text-based dataset called Greeting Cards. Dataset (GCD) that has abstract visual concepts. In our work, we propose to aggregate features from pretrained images and text embeddings to learn abstract visual concepts from GCD. This allows us to learn the text-modified image features, which combine complementary and redundant information from the multi-modal data streams into a single, meaningful feature. Secondly, the captions for the GCD dataset are computed with the pretrained CLIP-based image captioning model. Finally, we also demonstrate that the proposed the dataset is also useful for generating greeting card images using pre-trained text-to-image generation model.