We present TNCR, a new table dataset with varying image quality collected from free open source websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes.
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WikiTableSet is a large publicly available image-based table recognition dataset in three languages built from Wikipedia. WikiTableSet contains nearly 4 million English table images, 590K Japanese table images, 640k French table images with corresponding HTML representation, and cell bounding boxes. We build a Wikipedia table extractor WTabHTML and use this to extract tables (in HTML code format) from the 2022-03-01 dump of Wikipedia. In this study, we select Wikipedia tables from three representative languages, i.e., English, Japanese, and French; however, the dataset could be extended to around 300 languages with 17M tables using our table extractor. Second, we normalize the HTML tables following the PubTabNet format (separating table headers and table data, removing CSS and style tags). Finally, we use Chrome and Selenium to render table images from table HTML codes. This dataset provides a standard benchmark for studying table recognition algorithms in different languages or even
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