The DLoRA-TrOCR method can be extended to handle more diverse text types by incorporating additional training data that represent the new text types. For multi-lingual text, the model can be trained on datasets containing text in multiple languages, allowing it to learn the unique characteristics of each language. This would involve expanding the dataset to include samples of text in different languages and adjusting the model architecture to accommodate the variations in language structure and characters.
For handwritten text in different scripts, the model can be trained on datasets that include samples of handwritten text in various scripts. By exposing the model to a diverse range of handwriting styles and scripts, it can learn to recognize and interpret text accurately across different writing systems. This would require data preprocessing techniques to standardize the input data and ensure consistency in the representation of different scripts.
Additionally, fine-tuning the model on specific datasets that focus on multi-lingual or multi-script text recognition tasks can help improve its performance on these diverse text types. By fine-tuning the model parameters to adapt to the nuances of different languages and scripts, the DLoRA-TrOCR method can achieve better accuracy and generalization across a wider range of text types.