what is LayoutLM?

The complete form of LayoutLM is “Layout Language Model”

LayoutLM is a deep learning model architecture that Microsoft introduced in 2019 for the task of document layout analysis. Document layout analysis automatically identifies a document's structure and layout, including its text, images, and other visual elements.

LayoutLM is based on a pre-trained language model architecture called BERT (Bidirectional Encoder Representations from Transformers), which has achieved state-of-the-art results on a variety of natural language processing tasks. However, LayoutLM extends BERT by incorporating a visual representation of the document layout into the input embeddings used by the model.

Specifically, LayoutLM uses a two-stream architecture, where one stream processes the textual content of the document and the other stream processes the visual layout features. The visual stream consists of a series of convolutional layers that extract visual features from the document, such as the position, size, and color of text boxes, images, and other visual elements.

The output of the two streams is combined and fed into a multi-layer feedforward network that makes predictions about the structure and layout of the document, such as the location of headings, paragraphs, and other sections.

LayoutLM has achieved state-of-the-art results on several benchmark datasets for document layout analysis, demonstrating its effectiveness at integrating visual layout information with text-based language models. It has applications in areas such as document classification, information extraction, and natural language generation.

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