DocumentQA-Leveraging LayoutLMv3 for Next-Level Question Answering

With the increasing prevalence of digital documents in various domains, the demand for efficient and accurate question-answering (QA) systems has grown significantly. Traditional QA models primarily focus on text-based content, but the complex structure and visual elements present in documents pose unique challenges. This project explores the integration of LayoutLMv3, an advanced language and vision model, to elevate document question answering to the next level. The key role of LayoutLMv3 in document question answering (QA) lies in its ability to handle complex documents that contain not only textual information but also intricate visual elements like tables, images, and graphs. Traditional QA models typically focus on processing plain text and may struggle to comprehend the spatial arrangement and structure of documents. However, documents often contain vital information in their layout, which, if overlooked, can lead to inaccurate or incomplete answers. LayoutLMv3 overcomes this limitation by integrating both language and vision components into a single model. Through comprehensive tests on a variety of document datasets, this study shows the efficiency of DocumentQA. This study also analyzes the LayoutLMv3's interpretability and how it affects the way questions are answered. Through visualization techniques, various insights are gained on how the model analyses the document's layout, text, and interactions to generate accurate answers. The implications of this research extend beyond document QA, as LayoutLMv3's unique fusion of language and vision has broader applications in information retrieval, natural language processing, and document understanding tasks. The performance of LayoutLMv3 over LayoutLmv2 is also compared in this work showing efficient results.