In today's competitive and fast-evolving business environment, it is a critical time for
organizations to rethink how to make talent-related decisions in a quantitative manner …

Visually-situated language is ubiquitous—sources range from textbooks with diagrams to
web pages with images and tables, to mobile apps with buttons and forms. Perhaps due to …

Text is ubiquitous in our visual world, conveying crucial information, such as in documents,
websites, and everyday photographs. In this work, we propose UReader, a first exploration …

Recently leveraging large language models (LLMs) or multimodal large language models
(MLLMs) for document understanding has been proven very promising. However previous …

Structure information is critical for understanding the semantics of text-rich images, such as
documents, tables, and charts. Existing Multimodal Large Language Models (MLLMs) for …

Document understanding refers to automatically extract, analyze and comprehend
information from various types of digital documents, such as a web page. Existing Multi …

We present TextMonkey, a large multimodal model (LMM) tailored for text-centric tasks. Our
approach introduces enhancement across several dimensions: By adopting Shifted Window …

Abstract We propose Universal Document Processing (UDOP), a foundation Document AI
model which unifies text, image, and layout modalities together with varied task formats …

Image Transformer has recently achieved significant progress for natural image
understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) …

Visual information extraction (VIE) plays an important role in Document Intelligence.
Generally, it is divided into two tasks: semantic entity recognition (SER) and relation …