FigureScan

FigureScan is a term used for software tools that automatically locate, classify, and extract figures from digital documents. It is used to support document analytics, data extraction for meta-studies, and digital library workflows. FigureScan systems typically identify figure regions within pages, classify the figure type (photo, chart, diagram, map), and retrieve associated captions and labels.

Core techniques combine computer vision, layout analysis, and optical character recognition. Modern implementations may use convolutional neural networks to detect figures and to segment subpanels, while caption and legend text is captured via OCR. Some systems attempt to reconstruct structured data from charts by recognizing axes, scales, and data series, and may export results in JSON, CSV, or vector formats. Interoperability with PDF, HTML, and image inputs is common, and many tools provide APIs or plugins for document processing pipelines.

Applications and limitations. FigureScan is used in academic search engines, data mining, and reproducibility projects to improve indexing of visual content. It faces challenges with multi-part figures, low-resolution scans, and non-standard layouts, and performance depends on document quality and domain-specific features. The concept reflects a broader trend toward automating extraction of visual data from scholarly literature and other archives. See also related topics such as optical character recognition, diagram understanding, and chart data extraction.