Document understanding is a multimodal artificial intelligence technology that combines computer vision and natural language processing (NLP). It aims to enable machines to understand document content containing complex information such as text, images, tables, and page layout. Unlike traditional optical character recognition (OCR), which focuses solely on text transcription, document understanding systems not only need to recognize text but also analyze the spatial layout, visual elements, and semantic relationships between different content elements within the document, thereby achieving information extraction, content parsing, and knowledge organization.

The development of document understanding technology is built upon multiple fields, including OCR, document analysis, and pre-trained models. Early document processing systems primarily relied on OCR technology combined with manually designed rules to complete text recognition and field extraction. However, when faced with complex document formats (such as contracts, invoices, reports, and forms), the spatial relationships between texts were easily lost. In 2019, researchers at Microsoft Research Asia proposed… LayoutLM: Pre-training of Text and Layout for Document Image Understanding This work is the first to combine textual information with document layout information into a pre-trained model. By jointly modeling textual content and two-dimensional spatial position, it improves the model's ability to understand document structure and has become an important research work in the field of document understanding.

With the development of pre-trained models and multimodal learning, modern document understanding models are gradually integrating various information such as text, vision, and layout. Methods represented by the LayoutLM series of models further extend two-dimensional positional encoding, image features, and visual semantic representation, enabling models to handle more complex document scenarios. These technologies have been widely applied in fields such as Intelligent Document Processing (IDP), financial bill analysis, contract information extraction, enterprise document management, and medical document parsing.

Document understanding primarily addresses the problem that traditional OCR methods can only extract text content, struggling to understand document structure and semantic relationships. By jointly analyzing text content, bounding boxes, and visual features, the model can identify field relationships, table structures, and key information within a document, thereby reducing reliance on manually generated rule templates. Currently, document understanding has become an important technological direction in enterprise intelligent information processing and multimodal artificial intelligence systems.