简介：LayoutLM: Pre-training of Text and Layout for Document Image Understanding
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
Abstract
In this paper, we present LayoutLM: a novel approach for the pre-training of text and layout for document image understanding. The key idea of LayoutLM is to integrate layout information into language models for the purpose of enhancing document image understanding. By doing so, our model is able to jointly learn textual and layout features, thereby achieving improved performance in various document analysis tasks.
Introduction
Document image understanding is a crucial task in various real-world applications, such as automatic document processing, digital library construction, and intelligent decision-making. However, this task is challenging due to the presence of various layouts and formats in real-world documents. To address this challenge, LayoutLM focuses on the pre-training of both text and layout for document image understanding, integrating layout information into language models.
Related Work
Previous studies on document image understanding have mainly focused on either text or layout pre-training. Text pre-training typically involves the use of large-scale unlabeled text data to train language models, such as Transformer and BERT, for document classification, named entity recognition, and other NLP tasks. Layout pre-training, on the other hand, has sought to extract layout features using convolutional neural networks (CNNs) or variational autoencoders (VAEs) to perform tasks like document layout analysis and page segmentation.
However, these studies have mainly focused on either text or layout pre-training independently, neglecting the valuable information that can be obtained from the integration of both text and layout.
LayoutLM: Pre-training of Text and Layout for Document Image Understanding
In this paper, we present LayoutLM, a novel approach for the pre-training of text and layout for document image understanding. LayoutLM consists of two main components: a text encoder and a layout encoder. The text encoder is responsible for capturing the textual information present in the document, while the layout encoder extracts the layout information.
During pre-training, LayoutLM utilizes both text and layout information to learn representations for documents. The text encoder is trained using both textual and layout supervision, enabling it to capture the spatial relationships between text elements as well as the textual content itself. The layout encoder, on the other hand, is trained using only layout supervision to capture the spatial configuration of various layout elements present in the document.
By integrating both text and layout information during pre-training, LayoutLM is able to jointly learn textual and layout features. This allows it to not only understand the textual content of a document but also its layout structure, thereby achieving improved performance in various document analysis tasks.
Conclusion
In this paper, we presented LayoutLM: a novel approach for the pre-training of text and layout for document image understanding. By integrating layout information into language models during pre-training, our model is able to jointly learn textual and layout features. This results in improved performance in various document analysis tasks compared to existing methods that only consider text or layout independently.
Future work directions include exploring additional techniques to further enhance LayoutLM’s performance, such as incorporating multi-modal information (e.g., visual and lexical) during pre-training. Additionally, it would be interesting to investigate how LayoutLM performs on tasks other than document image understanding, such as natural language processing or computer vision tasks that involve understanding complex visual layouts.