Digital documents present knowledge in most areas of study, exchanging and communicating information in a portable way. To better use the knowledge embedded in an ever-growing information source, effective tools for automatic information extraction are needed. Tables are crucial information elements in documents of scientific nature. Most publications use tables to represent and report concrete findings of research. Current methods used to extract table data from PDF documents lack precision in detecting, extracting, and representing data from diverse layouts. We present the system TAble Organization (TAO) to automatically detect, extract and organize information from tables in PDF documents. TAO uses a processing, based on the k-nearest neighbor method and layout heuristics, to detect tables within a document and to extract table information. This system generates an enriched representation of the data extracted from tables in the PDF documents. TAO’s performance is comparable to other table extraction methods, but it overcomes some related work limitations and proves to be more robust in experiments with diverse document layouts.
['Martha Perez-Arriaga', 'Trilce Estrada', 'Soraya Abad-Mota']
TAO: System for Table Detection and Extraction from PDF Documents