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.

V-Doc 是一种使用文档图像和 PDF 文件的问答工具，主要用于帮助研究人员和普通非深度学习专家生成、处理和理解文档的视觉问答任务，支持使用文档图像生成和使用抽取式和生成式问答对，可用于各种数据集和模型，并且通过声明性、框架不可知平台高度可扩展。