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'''Table extraction''' is the process of recognizing and separating a [[Table (information)|table]] from a large document, possibly also recognizing individual rows, columns or elements.
It may be regarded as a special form of [[Information extraction|information extraction]].

Table extractions from webpages can take advantage of the special [[HTML element]]s that exist for tables, e.g., the "table" tag,
and programming libraries may implement table extraction from webpages.
The [[Python (programming language)|Python]] [[Software:Pandas|pandas]] software library can extract tables from HTML webpages via its read_html() function.

More challenging is table extraction from [[PDF]]s or [[Social:Image scanner|scanned images]], where there usually is no table-specific machine readable markup.<ref name="Table Extraction and Understanding for Scientific and Enterprise Applications">{{Cite Q | Q108170445 }}</ref>
Systems that extract data from tables in scientific [[PDF|PDFs]] have been described.<ref>{{Cite Q | Q108172460 }}</ref><ref>{{Cite Q | Q101086651 }}</ref>

[[Wikipedia]] presents some of its information in tables, 
and, e.g., 3.5 million tables can be extracted from the English Wikipedia.<ref>{{Cite Q | Q108215401 }}</ref>
Some of the tables have a specific format, e.g., the so-called [[Infobox|infobox]]es.
Large-scale table extraction of Wikipedia infoboxes forms one of the sources for  [[Software:DBpedia|DBpedia]].<ref>{{Cite Q | Q27910422 }}</ref>

Commercial web services for table extraction exist, e.g., [[Company:Amazon|Amazon]] Textract, [[Company:Google|Google's]] ''[[Software:Document AI|Document AI]]'', [[Company:IBM|IBM]] Watson Discovery, and [[Company:Microsoft|Microsoft]] Form Recognizer.<ref name="Table Extraction and Understanding for Scientific and Enterprise Applications"/>
Open source tools also exist, e.g., PDFFigures 2.0 that has been used in [[Semantic Scholar]].<ref>{{Cite Q | Q108172042 }}</ref>
In a comparison published in 2017, the researchers found the proprietary program [[Software:ABBYY FineReader|ABBYY FineReader]] to yield the best PDF table extraction performance among six different tools evaluated.<ref>{{Cite Q | Q108173686 }}</ref> In a 2023 benchmark evaluation,<ref>{{Citation |last1=Meuschke |first1=Norman |title=A Benchmark of PDF Information Extraction Tools Using a Multi-task and Multi-domain Evaluation Framework for Academic Documents |date=2023 |work=Information for a Better World: Normality, Virtuality, Physicality, Inclusivity |volume=13972 |pages=383–405 |editor-last=Sserwanga |editor-first=Isaac |url=https://link.springer.com/10.1007/978-3-031-28032-0_31 |place=Cham |publisher=Springer Nature Switzerland |language=en |doi=10.1007/978-3-031-28032-0_31 |isbn=978-3-031-28031-3 |last2=Jagdale |first2=Apurva |last3=Spinde |first3=Timo |last4=Mitrović |first4=Jelena |last5=Gipp |first5=Bela |editor2-last=Goulding |editor2-first=Anne |editor3-last=Moulaison-Sandy |editor3-first=Heather |editor4-last=Du |editor4-first=Jia Tina|arxiv=2303.09957 }}</ref> Adobe Extract,<ref>{{Cite web |title=Adobe PDF Extract API |url=https://developer.adobe.com/document-services/apis/pdf-extract/ |access-date=2024-03-15 |website=Adobe}}</ref> a cloud-based [[API]] that employs Adobe’s Sensei [[Artificial intelligence|AI]]-platform,<ref>{{Cite web |title=Experience Cloud AI Services with Adobe Sensei |url=https://business.adobe.com/products/sensei/adobe-sensei.html |access-date=2024-03-15 |website=Adobe}}</ref> performed best among five tools evaluated for table extraction. 

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[[Category:Natural language processing]]

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