Acceso usuarios
Matthias Frey
Table recognition and table extraction are important tasks in information extraction, especially in the domain of scholarly communication. In this domain tables are commonplace and contain valuable information. Many different automatic approaches for table recognition and extraction exist. Common to many of these approaches is the need for ground truth datasets, to train algorithms or to evaluate the results. In this paper we present the PDF Table Annotator, a web based tool for annotating elements and regions in PDF documents, in particular tables. The annotated data is intended to serve as a ground truth useful to machine learning algorithms for detecting table regions and table structure. To make the task of manual table annotation as convenient as possible, the tool is designed to allow an efficient annotation process that may spawn multiple session by multiple users. An evaluation is conducted where we compare our tool to three alternative ways of creating ground truth of tables in documents. Here we found that our tool overall provides an efficient and convenient way to annotate tables. In addition, our tool is particularly suitable for complex table structures, where it provided the lowest annotation time and the highest accuracy. Furthermore, our tool allows annotating tables following a logical or a functional model. Given that using our tool, ground truth datasets for table recognition and extraction are easier to produce, the quality of automatic tables extraction should greatly benefit.
