Visual Question Answering (VQA) is a complicated task that requires the
capability of simultaneously processing natural language and images. Initially,
this task was researched, focusing on methods to help machines understand
objects and scene contexts in images. However, some text appearing in the image
that carries explicit information about the full content of the image is not
mentioned. Along with the continuous development of the AI era, there have been
many studies on the reading comprehension ability of VQA models in the world.
As a developing country, conditions are still limited, and this task is still
open in Vietnam. Therefore, we introduce the first large-scale dataset in
Vietnamese specializing in the ability to understand text appearing in images,
we call it ViTextVQA (\textbf{Vi}etnamese \textbf{Text}-based \textbf{V}isual
\textbf{Q}uestion \textbf{A}nswering dataset) which contains \textbf{over
16,000} images and \textbf{over 50,000} questions with answers. Through
meticulous experiments with various state-of-the-art models, we uncover the
significance of the order in which tokens in OCR text are processed and
selected to formulate answers. This finding helped us significantly improve the
performance of the baseline models on the ViTextVQA dataset. Our dataset is
available at this
\href{this https URL}{link} for research
purposes.

通过对 OCR 文本中令牌的处理和选择顺序的细致实验，我们发现这一发现显著提高了在 ViTextVQA 数据集上基准模型的性能。

ViTextVQA：用于评估图像中越南文本理解的大规模视觉问答数据集

ViTextVQA: A Large-Scale Visual Question Answering Dataset for  Evaluating Vietnamese Text Comprehension in Images

Question answering (QA) and Machine Reading Comprehension (MRC) tasks have
significantly advanced in recent years due to the rapid development of deep
learning techniques and, more recently, large language models. At the same
time, many benchmark datasets have become available for QA and MRC tasks.
However, most existing large-scale benchmark datasets have been created
predominantly using synchronous document collections like Wikipedia or the Web.
Archival document collections, such as historical newspapers, contain valuable
information from the past that is still not widely used to train large language
models. To further contribute to advancing QA and MRC tasks and to overcome the
limitation of previous datasets, we introduce ChroniclingAmericaQA, a
large-scale dataset with 485K question-answer pairs created based on the
historical newspaper collection Chronicling America. Our dataset is constructed
from a subset of the Chronicling America newspaper collection spanning 120
years. One of the significant challenges for utilizing digitized historical
newspaper collections is the low quality of OCR text. Therefore, to enable
realistic testing of QA models, our dataset can be used in three different
ways: answering questions from raw and noisy content, answering questions from
cleaner, corrected version of the content, as well as answering questions from
scanned images of newspaper pages. This and the fact that ChroniclingAmericaQA
spans the longest time period among available QA datasets make it quite a
unique and useful resource.

基于历史报纸收藏物 Chronicling America 构建的大规模数据集 ChroniclingAmericaQA 是用于问题回答和机器阅读理解任务的独特而有用的资源。