{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:19:52Z","timestamp":1753881592304,"version":"3.41.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:p> The holistic understanding of the information contained in technical documents depends on the understanding of the document\u2019s individual modalities. These modalities are tables, graphics, diagrams, formulas, etc. and each of them is a standalone research topic that requires a different way of processing and understanding. These modalities, processed and combined with the document text, can introduce new techniques for visual question answering in documents. Thus, in this paper we present Pinakas, a methodology for automatic analysis of the internal tabular information that appears in technical documents and its modeling to stochastic Petri-net graphs. We focus only on tables that strictly abide by the IEEE format rules. The methodology presented here is divided into the following steps: (1) table detection, (2) table recognition, (3) table understanding. Qualitative results of Pinakas are demonstrated as proof of concept for the accurate extraction of information from three different types of tables. <\/jats:p>","DOI":"10.1142\/s0218213023500422","type":"journal-article","created":{"date-parts":[[2023,4,22]],"date-time":"2023-04-22T05:23:06Z","timestamp":1682140986000},"source":"Crossref","is-referenced-by-count":5,"title":["Pinakas: A Methodology for Deep Analysis of Tables in Technical Documents"],"prefix":"10.1142","volume":"32","author":[{"given":"Michail S.","family":"Alexiou","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, North Ave NW, Atlanta, 30332, Georgia, United States"}]},{"given":"Nikolaos G.","family":"Bourbakis","sequence":"additional","affiliation":[{"name":"Wright State University, 3640 Colonel Glenn Hwy, Dayton, 45435, Ohio, United States"}]}],"member":"219","published-online":{"date-parts":[[2023,6,30]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213023500422","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:41:35Z","timestamp":1688085695000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213023500422"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6]]},"references-count":0,"journal-issue":{"issue":"04","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["10.1142\/S0218213023500422"],"URL":"https:\/\/doi.org\/10.1142\/s0218213023500422","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2023,6]]},"article-number":"2350042"}}