The VimTS framework can be extended to handle more diverse text spotting scenarios by incorporating additional modules and training strategies tailored to specific types of text.
For multi-lingual text spotting, the model can be enhanced by including language-specific pre-trained models or embeddings to improve recognition accuracy for different languages. By fine-tuning the language-specific components of the model on multi-lingual datasets, VimTS can adapt to and effectively spot text in various languages.
To address handwritten text spotting, the framework can be augmented with specialized data augmentation techniques and training procedures that focus on the unique characteristics of handwritten text. By incorporating synthetic handwritten text data and leveraging transfer learning from pre-trained models on handwritten text datasets, VimTS can improve its performance in spotting handwritten text.
Additionally, the model can benefit from domain-specific adaptations and fine-tuning strategies to handle diverse text spotting scenarios effectively. By training on a diverse range of datasets representing different text types and styles, VimTS can enhance its generalization capabilities across various text spotting scenarios.