# HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images

Source: [HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images](<https://www.sota2.com/research/paper/68fa76571e209131cea6c7b9>)

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

Data updated: 2025-10-26T03:51:28.908Z

Authors: Yuchen Yang, Haoran Yan, Yanhao Chen, Qingqiang Wu, Qingqi Hong

Published: 2024-12-24T10:25:41.000Z

## Abstract

Vision Question Answering (VQA) tasks use images to convey critical information to answer text-based questions, which is one of the most common forms of question answering in real-world scenarios. Numerous vision-text models exist today and have performed well on certain VQA tasks. However, these models exhibit significant limitations in understanding human annotations on text-heavy images. To address this, we propose the Human Annotation Understanding and Recognition (HAUR) task. As part of this effort, we introduce the Human Annotation Understanding and Recognition-5 (HAUR-5) dataset, which encompasses five common types of human annotations. Additionally, we developed and trained our model, OCR-Mix. Through comprehensive cross-model comparisons, our results demonstrate that OCR-Mix outperforms other models in this task. Our dataset and model will be released soon .

## 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/2412.18327>)

[PDF](<https://arxiv.org/pdf/2412.18327.pdf>)