LLaVA-Read: Enhancing Reading Ability of Multimodal Language Models
This addresses a key limitation in multimodal AI for applications requiring reading text from images, though it appears incremental as it builds on existing encoder methods.
The paper tackles the problem of multimodal language models struggling with text-rich images by introducing LLaVA-Read, which uses dual visual encoders and a visual text encoder, achieving state-of-the-art performance in text-rich image understanding tasks.
Large multimodal language models have demonstrated impressive capabilities in understanding and manipulating images. However, many of these models struggle with comprehending intensive textual contents embedded within the images, primarily due to the limited text recognition and layout understanding ability. To understand the sources of these limitations, we perform an exploratory analysis showing the drawbacks of classical visual encoders on visual text understanding. Hence, we present LLaVA-Read, a multimodal large language model that utilizes dual visual encoders along with a visual text encoder. Our model surpasses existing state-of-the-art models in various text-rich image understanding tasks, showcasing enhanced comprehension of textual content within images. Together, our research suggests visual text understanding remains an open challenge and an efficient visual text encoder is crucial for future successful multimodal systems.