Quo Vadis Handwritten Text Generation for Handwritten Text Recognition?
This work addresses the problem of low-resource transcription for historical manuscripts, offering incremental improvements by evaluating existing HTG methods.
The paper tackled the challenge of improving Handwritten Text Recognition (HTR) for small, author-specific historical manuscripts by systematically comparing three state-of-the-art Handwritten Text Generation (HTG) models to assess their impact on HTR fine-tuning, providing quantitative guidelines for model selection.
The digitization of historical manuscripts presents significant challenges for Handwritten Text Recognition (HTR) systems, particularly when dealing with small, author-specific collections that diverge from the training data distributions. Handwritten Text Generation (HTG) techniques, which generate synthetic data tailored to specific handwriting styles, offer a promising solution to address these challenges. However, the effectiveness of various HTG models in enhancing HTR performance, especially in low-resource transcription settings, has not been thoroughly evaluated. In this work, we systematically compare three state-of-the-art styled HTG models (representing the generative adversarial, diffusion, and autoregressive paradigms for HTG) to assess their impact on HTR fine-tuning. We analyze how visual and linguistic characteristics of synthetic data influence fine-tuning outcomes and provide quantitative guidelines for selecting the most effective HTG model. The results of our analysis provide insights into the current capabilities of HTG methods and highlight key areas for further improvement in their application to low-resource HTR.