VATr++: Choose Your Words Wisely for Handwritten Text Generation

Area of Science:

• Computer Vision
• Machine Learning
• Natural Language Processing

Background:

• Styled Handwritten Text Generation (HTG) models, including GANs, Transformers, and Diffusion Models, have advanced significantly.
• The impact of input data (visual and textual) on HTG model training and performance remains understudied.
• Existing HTG approaches often face challenges with input pre-processing and training.

Purpose of the Study:

• To improve the performance and generalization of Styled-HTG models by addressing input preparation and training regularization.
• To establish a standardized evaluation protocol for HTG research.
• To conduct a comprehensive benchmark of current HTG methods for fair comparison.

Main Methods:

• Extended the VATr Styled-HTG approach with new strategies for input preparation.
• Implemented generalizable training regularization techniques.
• Developed and applied a standardized evaluation protocol for benchmarking HTG models.

Main Results:

• Proposed input preparation and regularization strategies enhance HTG model performance and generalization.
• A standardized evaluation protocol was introduced for HTG.
• A comprehensive benchmark of existing HTG approaches was conducted.

Conclusions:

• The proposed methods offer generally applicable solutions for improving Styled-HTG.
• Standardizing the evaluation protocol is crucial for advancing HTG research and enabling fair comparisons.
• This work lays the groundwork for more robust and comparable HTG strategies.