With the advancements in Natural Language Processing (NLP) and Computer Vision (CV), Document Visual Question Answering (Document VQA) has become an important research area both in industry and academia. Visual documents refer to documents containing various elements, such as images, tables, text paragraphs, and graphs. The challenge arises due to their multimodal nature and complex structure, where text and images must be processed together, often spanning multiple pages. Traditional question answering techniques are primarily designed for text-only or image-only inputs, making them ineffective when questions that require both text and visual elements. Even when these modalities are integrated, gaps can remain in how they interact and align.