Layout Analysis and Document Structure

Legal document analysis aims to retrieve crucial information by analyzing the contents of a legal document. In the case of legal contracts, the objective is to extract important elements like clauses (such as cancellation and exclusion) and entities (such as counterparties and termination date). Natural Language Processing (NLP) methods are primarily used for this purpose, but they require relevant and clean text blocks as input. However, segmenting the raw textual contents into such blocks is a challenging task. This is where Document Layout Analysis comes into the picture.

The main goal of Document Layout Analysis is to identify various object types in legal documents, such as titles, paragraphs, headers, and footers, by leveraging recurring visual patterns in previously seen documents. To achieve this, existing state-of-the-art vision models (such as those that are trained to detect common objects in real life – like cats and remotes) can be adapted to the specific features of legal document structures.

Document Layout Analysis not only helps in extracting relevant text blocks to feed NLP systems but also enables other purely visual tasks that may not be accomplished using text alone. For instance, it may lead to the ability to track the signature state of a document, or parse structured objects such as forms and tables which tend to occur frequently in the legal context. Ultimately, Document Layout Analysis aims to improve existing NLP methods while also laying the foundation for future vision-based systems.