LayoutLM

LayoutLM is a family of transformer-based models designed for document understanding. Introduced to improve tasks that require both textual content and document layout, LayoutLM integrates text tokens with precise 2D layout information from scanned or digitally produced documents. The model is pre-trained on large corpora of documents and can be fine-tuned for applications such as form understanding, key information extraction, and document classification.

In the core architecture, each token is represented not only by its textual embedding but also by layout information derived from the token’s bounding box coordinates on the page. This layout embedding captures x and y positions, width, and height, enabling the model to reason about spatial relationships between tokens. The token representations are processed by a Transformer encoder, allowing cross-attention across text and spatial cues. In its early form, LayoutLM relies on textual content plus layout, while later variants add multimodal components.

Pretraining objectives typically include masked language modeling to learn token semantics, along with objectives that encourage alignment between text and its visual/layout context. This pretraining enables the model to perform better on downstream tasks that depend on how information is arranged on a page, rather than on text alone.

LayoutLM has evolved through several iterations. LayoutLMv2 incorporates visual features extracted from the document image, enabling a more complete multimodal fusion of text, layout, and appearance. LayoutLMv3 further enhances cross-modal alignment and efficiency, aiming for stronger performance on complex document tasks. The model is commonly applied to key information extraction from forms and receipts, table understanding, and general document classification, with benchmarks on datasets such as FUNSD, SROIE, and RVL-CDIP.

Open-source implementations and pre-trained weights are available in PyTorch and through libraries like Hugging Face Transformers, facilitating adoption in research and industry.