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

• URL: http://arxiv.org/abs/2412.18327v1
• Date: Tue, 24 Dec 2024 10:25:41 GMT
• Title: HAUR: Human Annotation Understanding and Recognition Through Text-Heavy Images
• Authors: Yuchen Yang, Haoran Yan, Yanhao Chen, Qingqiang Wu, Qingqi Hong,
• Abstract summary: Vision Question Answering (VQA) tasks use images to convey critical information to answer text-based questions. Our dataset and model will be released soon.
• Score: 4.468589513127865
• License:
• 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 .

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