Handwritten text generation (HTG) aims to synthesize handwritten samples by imitating a specific writer, which has a wide range of applications and thus has significant research value. However, current studies on HTG are confronted with a main bottleneck: dominant models lack the ability to perceive and integrate handwriting styles, which affects the realism of the synthesized samples. In this paper, we propose GL-GAN, which effectively captures and integrates global and local styles. Specifically, we propose a Hybrid Style Encoder (HSE) that combines a state space model (SSM) and convolution to capture multilevel style features through various receptive fields. The captured style features are then fed to the proposed Dynamic Feature Enhancement Module (DFEM), which integrates these features by adaptively modeling the entangled relationships between multilevel styles and removing redundant details. Extensive experiments on two widely used handwriting datasets demonstrate that our GL-GAN is an effective HTG model and outperforms state-of-the-art models remarkably. Our code is publicly available at:https://github.com/Fyzjym/GL-GAN.

Constructing a highly accurate handwritten OCR system requires large amounts of high-quality training data, yet data collection is labor-intensive and costly. With the advance of generative models, high-quality synthetic images have been applied to enhance handwritten text recognition (HTR) models, a technique termed data expansion. However, we find that the generated data may sometimes provide only insignificant improvements to HTR. We investigate the causes behind the small gains from the perspectives of both data expansion and data augmentation. We reveal a balancing mechanism in HTR that stronger data expansion should be paired with weaker data augmentation. Informed by these insights, we propose SmartExp, a purely data-driven strategy without introducing any extra data or computational costs. Extensive experiments on three widely used benchmark datasets demonstrate that our SmartExp strategy can significantly improve various HTR models. Our code is publicly available at: https://github.com/Fyzjym/SmartExp.

In the field of handwritten recognition, it usually needs to collect a large number of handwriting samples to obtain better results. However, it is so time-consuming and expensive that such a large number of samples cannot be collected manually. There is an effective solution to the above problem through synthesizing handwriting samples. In this study, an approach based on Transformer and deformable convolution has been proposed to generate much more realistic handwriting samples. To be specific, a Transformer has been utilized to capture the relations between writing style and text strings. By this way, global features (e.g. thickness of stroke, slant and so on) of writing style can be obtained. Meanwhile, a feature deformation fusion (FDF) module has been designed for obtaining local features (i.e. personalization) of writing style. Moreover, a focal frequency loss (FFL) is employed to solve the problem of pen-level artifacts. Experimental results demonstrate that the proposed approach can be competent for the task of handwriting samples generation and outperforms various baseline and state-of-the-art methods.

The particular word formation manner of the Mongolian language makes its vocabulary reach millions. It is tough to manually collect a Mongolian handwritten word images dataset covering all words. In order to realize the automatic generation of Mongolian handwritten word images, an approach based on generative adversarial networks has been proposed in this paper, which is called Mongolian Generator (MG). The proposed MG generates Mongolian handwritten word images with better writing details and more accurate text content. Also, it can generate handwritten word images based on a specific writing style or text content. Moreover, the perceptual adversarial loss has been integrated into the MG, which guarantees that the generated handwritten word images appear more realistic. The experimental results indicate that the performance of the proposed MG outperforms several baselines and state-of-the-art models.

We present a multi-layer network propagation model including both offline and online social networks, which aims to imitate the interaction between the offline actual individuals and the online social networking ones throughout the propagation process. After that, we utilize the improved SIR model to simulate the transmission in social networks under the impact of actual individuals. In our model, the individual layer adopts the generalized linear threshold model, while the social network layer uses the improved SIR model proposed in this paper. We apply these two propagation models into a three-layer network with inter-layer links, and use Monte Carlo simulation to analyze the model dynamic properties. Furthermore, we make use of mean-field approximations to obtain the analytical solutions to the critical thresholds regarding the epidemic process. We discuss the effect of different factors on the model results, and observe whether the model has different dynamic performance from the classical SIR model as well. The present results will provide some deeper insights into how various contagion phenomena spread within many real-world networks.

Attack vulnerability is one of the fundamental properties of complex interdependent networks, which has attracted a lot of research efforts in recent years. In this paper, we study the effect of degree heterogeneity on structural vulnerability of interdependent networks when they suffer targeted attacks. Firstly, we construct an interdependent system model composed of two network components. By adjusting a parameter, the extent of degree heterogeneity of each network can be controlled. Then, numerical simulations are implemented to demonstrate the impact of degree heterogeneity. The research results verify that the vulnerability of both single and interdependent networks can be affected by degree heterogeneity, that is, degree heterogeneity can significantly increase the vulnerability of these networks. Additionally, the existence of interdependent links between two networks makes both networks much more vulnerable against targeted attacks. In particular, when coupling preference is taken into account, it is found that compared with disassortative coupling and random coupling, assortative coupling can lead to a more fragile interdependent system. Our research results can give some help on the deep understanding of structural vulnerability of complex interdependent networks, and shed some light on the design of robust systems.