StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation

• URL: http://arxiv.org/abs/2407.15608v1
• Date: Mon, 22 Jul 2024 13:08:30 GMT
• Title: StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation
• Authors: Nauman Riaz, Saifullah Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed,
• Abstract summary: StylusAI is designed to adapt and integrate the stylistic nuances of one language's handwriting into another. To support the development and evaluation of StylusAI, we present the lqDeutscher Handschriften-Datensatzrq(DHSD) dataset.
• Score: 4.891597567642704
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this study, we introduce StylusAI, a novel architecture leveraging diffusion models in the domain of handwriting style generation. StylusAI is specifically designed to adapt and integrate the stylistic nuances of one language's handwriting into another, particularly focusing on blending English handwriting styles into the context of the German writing system. This approach enables the generation of German text in English handwriting styles and German handwriting styles into English, enriching machine-generated handwriting diversity while ensuring that the generated text remains legible across both languages. To support the development and evaluation of StylusAI, we present the \lq{Deutscher Handschriften-Datensatz}\rq~(DHSD), a comprehensive dataset encompassing 37 distinct handwriting styles within the German language. This dataset provides a fundamental resource for training and benchmarking in the realm of handwritten text generation. Our results demonstrate that StylusAI not only introduces a new method for style adaptation in handwritten text generation but also surpasses existing models in generating handwriting samples that improve both text quality and stylistic fidelity, evidenced by its performance on the IAM database and our newly proposed DHSD. Thus, StylusAI represents a significant advancement in the field of handwriting style generation, offering promising avenues for future research and applications in cross-linguistic style adaptation for languages with similar scripts.

Related papers

• Towards Visual Text Design Transfer Across Languages [49.78504488452978]
  We introduce a novel task of Multimodal Style Translation (MuST-Bench) MuST-Bench is a benchmark designed to evaluate the ability of visual text generation models to perform translation across different writing systems. In response, we introduce SIGIL, a framework for multimodal style translation that eliminates the need for style descriptions.
  arXiv  Detail & Related papers  (2024-10-24T15:15:01Z)
• DiffusionPen: Towards Controlling the Style of Handwritten Text Generation [7.398476020996681]
  DiffusionPen (DiffPen) is a 5-shot style handwritten text generation approach based on Latent Diffusion Models. Our approach captures both textual and stylistic characteristics of seen and unseen words and styles, generating realistic handwritten samples. Our method outperforms existing methods qualitatively and quantitatively, and its additional generated data can improve the performance of Handwriting Text Recognition (HTR) systems.
  arXiv  Detail & Related papers  (2024-09-09T20:58:25Z)
• Learning to Generate Text in Arbitrary Writing Styles [6.7308816341849695]
  It is desirable for language models to produce text in an author-specific style on the basis of a potentially small writing sample. We propose to guide a language model to generate text in a target style using contrastively-trained representations that capture stylometric features.
  arXiv  Detail & Related papers  (2023-12-28T18:58:52Z)
• MetaScript: Few-Shot Handwritten Chinese Content Generation via Generative Adversarial Networks [15.037121719502606]
  We propose MetaScript, a novel content generation system designed to address the diminishing presence of personal handwriting styles in the digital representation of Chinese characters. Our approach harnesses the power of few-shot learning to generate Chinese characters that retain the individual's unique handwriting style and maintain the efficiency of digital typing.
  arXiv  Detail & Related papers  (2023-12-25T17:31:19Z)
• StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model [64.26721402514957]
  We propose StylerDALLE, a style transfer method that uses natural language to describe abstract art styles. Specifically, we formulate the language-guided style transfer task as a non-autoregressive token sequence translation. To incorporate style information, we propose a Reinforcement Learning strategy with CLIP-based language supervision.
  arXiv  Detail & Related papers  (2023-03-16T12:44:44Z)
• StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing [73.81778485157234]
  Long texts usually involve more complicated author linguistic preferences such as discourse structures than sentences. We formulate the task of non-parallel story author-style transfer, which requires transferring an input story into a specified author style. We use an additional training objective to disentangle stylistic features from the learned discourse representation to prevent the model from degenerating to an auto-encoder.
  arXiv  Detail & Related papers  (2022-08-29T08:47:49Z)
• SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text [35.83345711291558]
  We propose a novel method that can synthesize parameterized and controllable handwriting Styles for arbitrary-Length and Out-of-vocabulary text. We embed the text content by providing an easily obtainable printed style image, so that the diversity of the content can be flexibly achieved. Our method can synthesize words that are not included in the training vocabulary and with various new styles.
  arXiv  Detail & Related papers  (2022-02-23T12:13:27Z)
• Letter-level Online Writer Identification [86.13203975836556]
  We focus on a novel problem, letter-level online writer-id, which requires only a few trajectories of written letters as identification cues. A main challenge is that a person often writes a letter in different styles from time to time. We refer to this problem as the variance of online writing styles (Var-O-Styles)
  arXiv  Detail & Related papers  (2021-12-06T07:21:53Z)
• SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators [67.54204685189255]
  We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
  arXiv  Detail & Related papers  (2021-05-21T18:34:21Z)
• Handwriting Transformers [98.3964093654716]
  We propose a transformer-based styled handwritten text image generation approach, HWT, that strives to learn both style-content entanglement and global and local writing style patterns. The proposed HWT captures the long and short range relationships within the style examples through a self-attention mechanism. Our proposed HWT generates realistic styled handwritten text images and significantly outperforms the state-of-the-art demonstrated.
  arXiv  Detail & Related papers  (2021-04-08T17:59:43Z)
• Generating Handwriting via Decoupled Style Descriptors [28.31500214381889]
  We introduce the Decoupled Style Descriptor model for handwriting. It factors both character- and writer-level styles and allows our model to represent an overall greater space of styles. In experiments, our generated results were preferred over a state of the art baseline method 88% of the time.
  arXiv  Detail & Related papers  (2020-08-26T02:52:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.