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Therefore, this study treats the correct position of a table as a tolerance region. Additionally, to overcome the limitations of existing datasets in the materials domain, we collected 1183 samples from scientific literature in the materials field and created the MatTab dataset, annotating the tables with tolerance regions. This paper use Cascade RCNN with Swin Transformer as baseline models, and BLC is utilized to optimize the detection results. Experimental results demonstrate significant improvements with BLC at an IOU of 0.95 on the MatTab, ICDAR2019, and ICDAR2017 datasets. In MatTab, the percentage of correctly detected complete and pure tables increased from 72.3% to 82.1%.<\/jats:p>","DOI":"10.1007\/s40747-023-01235-9","type":"journal-article","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T08:01:31Z","timestamp":1696060891000},"page":"1703-1714","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Improving table detection for document images using boundary"],"prefix":"10.1007","volume":"10","author":[{"given":"Yingli","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jianfeng","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Guangtao","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Tao","family":"Shen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,30]]},"reference":[{"key":"1235_CR1","doi-asserted-by":"publisher","unstructured":"Itonori K (1993) Table structure recognition based on textblock arrangement and ruled line position. 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