# Document Collection Visual Question Answering

Source: [Document Collection Visual Question Answering](<https://www.sota2.com/research/paper/68fa6ef11e209131cea430d4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:10:39.531Z

Authors: Rub\\\`en Tito, Dimosthenis Karatzas, Ernest Valveny

Published: 2021-04-27T18:05:48.000Z

## Abstract

Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. To address this problem, we introduce Document Collection Visual Question Answering (DocCVQA) a new dataset and related task, where questions are posed over a whole collection of document images and the goal is not only to provide the answer to the given question, but also to retrieve the set of documents that contain the information needed to infer the answer. Along with the dataset we propose a new evaluation metric and baselines which provide further insights to the new dataset and task.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2104.14336>)

[PDF](<https://arxiv.org/pdf/2104.14336.pdf>)