layoutanalysis

Layout analysis is the process of identifying and interpreting the structural organization of a document image or page. It aims to delineate regions such as text blocks, titles, figures, tables, and other content, and to establish their reading order and logical relationships. It is a key step in document image analysis, digital archiving, and automated information extraction.

Typical tasks include page segmentation (partitioning the page into zones), zone classification (assigning semantic labels), reading-order determination, and structural reconstruction (building a hierarchy that represents the document’s logical organization). Systems must handle multi-column layouts, mixed content, rotated text, and varying quality of the input.

Approaches range from traditional rule-based methods to modern machine learning. Classical techniques use connected-components analysis, projection profiles, and heuristics. Deep learning methods employ region proposal networks, semantic or instance segmentation (for example, U-Net or Mask R-CNN variants), graph-based models, and transformer architectures that incorporate text coordinates. Some models integrate textual content with layout signals through specialized representations that jointly model content and geometry.

Outputs typically include bounding boxes or segmentation masks for regions, labels, and sometimes a hierarchical structure or reading order. These outputs support downstream tasks such as optical character recognition, form and table extraction, accessibility enhancement, search indexing, and automated document processing.

Prominent datasets and benchmarks include PubLayNet and DocBank for layout-aware document understanding, as well as various ICDAR competitions that evaluate layout analysis components. Evaluation metrics commonly cover intersection-over-union (IoU), precision, recall, F1 for region detection, and text recognition accuracy for content extraction.