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StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation

Nauman Riaz, Saifullah Saifullah, Stefan Agne, Andreas Dengel, Sheraz Ahmed

TL;DR

StylusAI tackles the challenge of cross-language handwriting style transfer between English and German by employing a diffusion-based architecture conditioned on target text, writer style, and a synthetic printed text image. A new German handwriting dataset, DHSD, with 37 styles is introduced to support training and benchmarking alongside IAM. The approach outperforms prior methods on both IAM and DHSD in terms of textual accuracy (CER) and writer-style fidelity, including successful but nuanced Eng-DHSD adaptation. Overall, the work advances cross-linguistic handwriting generation and provides a valuable dataset and methodology for future cross-script style adaptation research.

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.

StylusAI: Stylistic Adaptation for Robust German Handwritten Text Generation

TL;DR

StylusAI tackles the challenge of cross-language handwriting style transfer between English and German by employing a diffusion-based architecture conditioned on target text, writer style, and a synthetic printed text image. A new German handwriting dataset, DHSD, with 37 styles is introduced to support training and benchmarking alongside IAM. The approach outperforms prior methods on both IAM and DHSD in terms of textual accuracy (CER) and writer-style fidelity, including successful but nuanced Eng-DHSD adaptation. Overall, the work advances cross-linguistic handwriting generation and provides a valuable dataset and methodology for future cross-script style adaptation research.

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.
Paper Structure (22 sections, 4 equations, 6 figures, 4 tables)

This paper contains 22 sections, 4 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Diffusion Models
  5. Forward Diffusion
  6. Reverse Diffusion
  7. StylusAI: The Proposed Approach
  8. Transformer Encoder and Style Conditioning
  9. Forward Diffusion and Training
  10. Reverse Diffusion: Denoising
  11. Experimental Setup
  12. Datasets
  13. IAM:
  14. Deutscher Handschriften-Datensatz (DHSD):
  15. IAM + DHSD
  16. ...and 7 more sections

Figures (6)

  • Figure 1: Few images showcasing a selection of written German words, with a particular focus on the unique characters found in the German alphabet, including umlauts and the eszett.
  • Figure 2: Overview of the Proposed Architecture.
  • Figure 3: These images provide a glimpse into the diversity of IAM dataset, showcasing a variety of unique handwriting styles
  • Figure 4: These images provide a glimpse into the diversity of our proposed DHSD, showcasing a variety of unique handwriting styles.
  • Figure 5: German text samples generated by StylusAI, emulating different IAM writer styles, where good generations are those in which the German characters have been better adapted to the English writer styles compared to average and poor generations.
  • ...and 1 more figures