# ICDAR 2021 Competition on Document VisualQuestion Answering

Source: [ICDAR 2021 Competition on Document VisualQuestion Answering](<https://www.sota2.com/research/paper/68fa6b371e209131cea2cfba>)

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

Data updated: 2025-10-25T23:43:15.362Z

Authors: Rub\\\`en Tito, Minesh Mathew, C.V. Jawahar, Ernest Valveny, Dimosthenis Karatzas

Published: 2021-11-10T06:52:07.000Z

## Abstract

In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infographics VQA is based on a new dataset of more than 5,000 infographics images and 30,000 question-answer pairs. The winner methods have scored 0.6120 ANLS in Infographics VQA task, 0.7743 ANLSL in Document Collection VQA task and 0.8705 ANLS in Single Document VQA. We present a summary of the datasets used for each task, description of each of the submitted methods and the results and analysis of their performance. A summary of the progress made on Single Document VQA since the first edition of the DocVQA 2020 challenge is also presented.

## 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.

- [Document Classification on RVL-CDIP (test)](<https://www.sota2.com/research/sota/document-classification-on-rvl-cdip-test.md>): Document Classification; RVL-CDIP (test); Accuracy: 95.25.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2111.05547>)

[PDF](<https://arxiv.org/pdf/2111.05547.pdf>)