Abstract
Legal documents often have a complex layout with many different headings, headers and footers, side notes, etc. For the further processing, it is important to extract these individual components correctly from a legally binding document, for example a signed PDF. A common approach to do so is to classify each (text) region of a page using its geometric and textual features. This approach works well, when the training and test data have a similar structure and when the documents of a collection to be analyzed have a rather uniform layout. We show that the use of global page properties can improve the accuracy of text element classification: we first classify each page into one of three layout types. After that, we can train a classifier for each of the three page types and thereby improve the accuracy on a manually annotated collection of 70 legal documents consisting of 20,938 text elements. When we split by page type, we achieve an improvement from 0.95 to 0.98 for single-column pages with left marginalia and from 0.95 to 0.96 for double-column pages. We developed our own feature-based method for page layout detection, which we benchmark against a standard implementation of a CNN image classifier.
Highlights
Many documents are only available as PDF
We show that the use of global page properties can improve the accuracy of text element classification: we first classify each page into one of three layout types
We can train a classifier for each of the three page types and thereby improve the accuracy on a manually annotated collection of 70 legal documents consisting of 20,938 text elements
Summary
Many documents are only available as PDF. This is especially the case for legal documents where one exact copy including layout and signatures is distributed and archived. Extracting the text from a legal document is often challenging since e.g. contracts often have a complex structure with lists, footnotes, side notes, multiple columns, headers and footers and so on. Contracts often consist of several parts, like address page, signature page, project description, terms of service etc. Which each may have a completely different layout. In order to extract texts from a PDF we first identify characters, regions of closely neighbouring characters (words) and regions with dense text.
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