Optical Character Recognition - Visual Question Answering (OCR-VQA) is the
task of answering text information contained in images that have just been
significantly developed in the English language in recent years. However, there
are limited studies of this task in low-resource languages such as Vietnamese.
To this end, we introduce a novel dataset, ViOCRVQA (Vietnamese Optical
Character Recognition - Visual Question Answering dataset), consisting of
28,000+ images and 120,000+ question-answer pairs. In this dataset, all the
images contain text and questions about the information relevant to the text in
the images. We deploy ideas from state-of-the-art methods proposed for English
to conduct experiments on our dataset, revealing the challenges and
difficulties inherent in a Vietnamese dataset. Furthermore, we introduce a
novel approach, called VisionReader, which achieved 0.4116 in EM and 0.6990 in
the F1-score on the test set. Through the results, we found that the OCR system
plays a very important role in VQA models on the ViOCRVQA dataset. In addition,
the objects in the image also play a role in improving model performance. We
open access to our dataset at link (this https URL)
for further research in OCR-VQA task in Vietnamese.

通过提供一个新的数据集 ViOCRVQA，这篇研究论文在 OCR-VQA 任务中引入了一种称为 VisionReader 的新方法，并对该数据集进行了实验，揭示了越南数据集所固有的挑战和困难。