{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:58:39Z","timestamp":1777705119130,"version":"3.51.4"},"reference-count":12,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,1,10]]},"abstract":"<jats:p>Tables are commonly used for effective and compact representation of relational information across the data in diverse document classes like scientific papers, financial statements, newspaper articles, invoices, or product descriptions. However, table structure detection is a relatively simple process for humans, but recognizing precise table structure is still a computer vision challenge. Further, innumerable possible table layouts increase the risk of automatic topic modeling and understanding the capability of each table from the generic document. This paper develops the framework to recognize the table structure from the Compound Document Image(CDI). Initially, the bilateral filter is designed for image transformation, enhancing CDI quality. An improved binarization-Sauvola algorithm (IBSA) is proposed to degrade the tables with uneven illumination, low contrast, and uniform background. The morphological Thinning method extracts the line from the table. The masking approach extracts the row and column from the table. Finally, the ResNet Attention model optimized over Black Widow optimization-based mutual exclusion (BWME) is developed to recognize the table structure from the document images. The UNLV, TableBank, and ICDAR-2013 table competition datasets are used to evaluate the proposed framework\u2019s performance. Precision and accuracy are the metrics considered for evaluating the proposed framework performance. From the experimental results, the proposed framework achieved a precision value of 96.62 and the accuracy value of 94.34, which shows the effectiveness of the proposed approach\u2019s performance.<\/jats:p>","DOI":"10.3233\/jifs-232646","type":"journal-article","created":{"date-parts":[[2023,11,21]],"date-time":"2023-11-21T11:02:31Z","timestamp":1700564551000},"page":"1101-1114","source":"Crossref","is-referenced-by-count":1,"title":["Table structure recognition using black widow based mutual exclusion and RESNET attention model"],"prefix":"10.1177","volume":"46","author":[{"given":"Devendra","family":"Tiwari","sequence":"first","affiliation":[{"name":"University College of Engineering and Technology, Bikaner, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anand","family":"Gupta","sequence":"additional","affiliation":[{"name":"Netaji Subhas University of Technology, New Delhi, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"11","key":"10.3233\/JIFS-232646_ref3","first-page":"771","article-title":"Table detection using deep learning","volume":"1","author":"Gilani","year":"2017","journal-title":"IEEE"},{"issue":"4","key":"10.3233\/JIFS-232646_ref5","doi-asserted-by":"crossref","first-page":"3203","DOI":"10.3934\/mbe.2020182","article-title":"Robust table recognition for printed document images","volume":"17","author":"Liang","year":"2020","journal-title":"Mathematical Biosciences and Engineering"},{"issue":"7671","key":"10.3233\/JIFS-232646_ref6","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"10.3233\/JIFS-232646_ref7","doi-asserted-by":"crossref","first-page":"108565","DOI":"10.1016\/j.patcog.2022.108565","article-title":"Split, embed and merge: An accurate table structure recognizer","volume":"126","author":"Zhang","year":"2022","journal-title":"Pattern Recognition"},{"key":"10.3233\/JIFS-232646_ref8","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.commatsci.2016.05.034","article-title":"Image driven machine learning methods for microstructure recognition","volume":"123","author":"Chowdhury","year":"2016","journal-title":"Computational Materials Science"},{"key":"10.3233\/JIFS-232646_ref10","doi-asserted-by":"crossref","unstructured":"Burdick D. , Danilevsky M. , Evfimievski A.V. , Katsis Y. and Wang N. , Table extraction and understanding for scientific and enterprise applications, 13(12) (2020), 3433\u20133436.","DOI":"10.14778\/3415478.3415563"},{"key":"10.3233\/JIFS-232646_ref12","unstructured":"Patil H. , Gaikwad V. , Pawar D. and Nikam M. , Intelligence extraction using machine learning technics, Intelligence 6(06) (2019)."},{"issue":"3","key":"10.3233\/JIFS-232646_ref20","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3390\/technologies7030065","article-title":"Data-driven recognition and extraction of pdf document elements","volume":"7","author":"Hansen","year":"2019","journal-title":"Technologies"},{"issue":"1","key":"10.3233\/JIFS-232646_ref21","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10032-019-00317-0","article-title":"A framework for information extraction from tables in biomedical literature","volume":"22","author":"Milosevic","year":"2019","journal-title":"International Journal on Document Analysis and Recognition (IJDAR)"},{"issue":"3","key":"10.3233\/JIFS-232646_ref22","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.ipm.2019.01.008","article-title":"Texus: A unified framework for extracting and understanding tables in pdf documents","volume":"56","author":"Rastan","year":"2019","journal-title":"Information Processing & Management"},{"issue":"1","key":"10.3233\/JIFS-232646_ref24","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.imavis.2003.08.005","article-title":"A common framework for nonlinear diffusion, adaptive smoothing, bilateral filtering and mean shift","volume":"22","author":"Barash","year":"2004","journal-title":"Image and Vision Computing"},{"key":"10.3233\/JIFS-232646_ref27","doi-asserted-by":"crossref","first-page":"103249","DOI":"10.1016\/j.engappai.2019.103249","article-title":"Black widow optimization algorithm: A novel meta-heuristic approach for solving engineering optimization problems","volume":"87","author":"Hayyolalam","year":"2020","journal-title":"Engineering Applications of Artificial Intelligence"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232646","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:42:57Z","timestamp":1777455777000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":12,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232646","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,10]]}}}