V-Doc : Visual questions answers with Documents

TL;DR

V-Doc is a versatile question-answering tool for document images and PDFs that supports extractive and abstractive answering, aiding researchers and non-experts in understanding visual documents.

Contribution

It introduces a flexible, extensible platform for visual question answering on documents, supporting multiple datasets and models with a focus on usability for non-deep learning experts.

Findings

Supports both extractive and abstractive QA methods

Compatible with various datasets and models

Highly extensible and framework-agnostic

Abstract

We propose V-Doc, a question-answering tool using document images and PDF, mainly for researchers and general non-deep learning experts looking to generate, process, and understand the document visual question answering tasks. The V-Doc supports generating and using both extractive and abstractive question-answer pairs using documents images. The extractive QA selects a subset of tokens or phrases from the document contents to predict the answers, while the abstractive QA recognises the language in the content and generates the answer based on the trained model. Both aspects are crucial to understanding the documents, especially in an image format. We include a detailed scenario of question generation for the abstractive QA task. V-Doc supports a wide range of datasets and models, and is highly extensible through a declarative, framework-agnostic platform.