Artistic typography is a technique to visualize the meaning of input character in an imaginable and readable manner. With powerful text-to-image diffusion models, existing methods directly design the overall geometry and texture of input character, making it challenging to ensure both creativity and legibility. In this paper, we introduce a dual-branch and training-free method, namely VitaGlyph, enabling flexible artistic typography along with controllable geometry change to maintain the readability. The key insight of VitaGlyph is to treat input character as a scene composed of Subject and Surrounding, followed by rendering them under varying degrees of geometry transformation. The subject flexibly expresses the essential concept of input character, while the surrounding enriches relevant background without altering the shape. Specifically, we implement VitaGlyph through a three-phase framework: (i) Knowledge Acquisition leverages large language models to design text descriptions of subject and surrounding. (ii) Regional decomposition detects the part that most matches the subject description and divides input glyph image into subject and surrounding regions. (iii) Typography Stylization firstly refines the structure of subject region via Semantic Typography, and then separately renders the textures of Subject and Surrounding regions through Controllable Compositional Generation. Experimental results demonstrate that VitaGlyph not only achieves better artistry and readability, but also manages to depict multiple customize concepts, facilitating more creative and pleasing artistic typography generation. Our code will be made publicly at https://github.com/Carlofkl/VitaGlyph.

Text Generation is a task in Natural Language Processing (NLP) in which text is generated with some constraints such as initial characters or initial words. We come across this task in our day-to-day applications such as character/word/sentence predictions while typing texts in Gmail, Google Docs, Smartphone keyboard, and chatbot. Understanding of text generation forms the base to advanced NLP tasks such as Neural Machine Translation. This article discusses the text generation task to predict the next character given its previous characters. It employs a recurrent neural network with LSTM layers to achieve the task. The deep learning process will be carried out using TensorFlow's Keras, a high-level API.

The aim of this article is to delineate a theory of interaction semantics and thereby provide a proper understanding of the "meaning" of the exchanged characters within an interaction. The idea is to describe the interaction (between discrete systems) by a mechanism that depends on information exchange, that is, on the identical naming of the "exchanged" characters -- by a protocol. Complementing a nondeterministic protocol with decisions to a game in its interactive form (GIF) makes it interpretable in the sense of an execution. The consistency of such a protocol depends on the particular choice of its sets of characters. Thus, assigning a protocol its sets of charaacters makes it consistent or not, creating a fulfillment relation. The interpretation of the characters during GIF execution results in their meaning. The proposed theory of interaction semantics is consistent with the model of information transport and processing, it has a clear relation to models of formal semantics, it accounts for the fact that the meaning of a character is invariant against renaming and locates the concept of meaning in the technical description of interactions. It defines when two different characters have the same meaning and what an "interpretation" and what an "interpretation context" is as well as under which conditions meaning is compositional.