Juliusz Ziomek
Stuart E. Middleton
ABSTRACT
Understanding and extracting tables from documents is a research problem that has been studied for decades. Table structure recognition is the labelling of components within a detected table, which can be detected automatically or manually provided. This paper presents the GloSAT historical measurement table dataset designed to train table structure recognition models for use in downstream historical data rescue applications. The dataset contains 500 scanned and manually annotated images of pages from meteorological measurement logbooks. We enhance standard full table and individual cell annotations by adding additional annotations for headings, headers, and table bodies. We also provide annotations for coarse segmentation cells consisting of multiple data cells logically grouped by ruling lines of ink or whitespace in the table, which often represent data cells that are semantically grouped. Our dataset annotations are provided in VOC2007 and ICDAR-2019 Competition on Table Detection and Recognition (cTDaR-19) XML formats, and our dataset can easily be aggregated with the cTDaR-19 dataset. We report results running a series of benchmark algorithms on our new dataset, concluding that post-processing is very important for performance, and that page style is not as significant a feature as table type on model performance.
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