  In the era of Large Language Models (LLMs), tremendous strides have been made
in the field of multimodal understanding. However, existing advanced algorithms
are limited to effectively utilizing the immense representation capabilities
and rich world knowledge inherent to these large pre-trained models, and the
beneficial connections among tasks within the context of text-rich scenarios
have not been sufficiently explored. In this work, we introduce UniDoc, a novel
multimodal model equipped with text detection and recognition capabilities,
which are deficient in existing approaches. Moreover, UniDoc capitalizes on the
beneficial interactions among tasks to enhance the performance of each
individual task. To implement UniDoc, we perform unified multimodal instruct
tuning on the contributed large-scale instruction following datasets.
Quantitative and qualitative experimental results show that UniDoc sets
state-of-the-art scores across multiple challenging benchmarks. To the best of
our knowledge, this is the first large multimodal model capable of simultaneous
text detection, recognition, spotting, and understanding.
