  Automated document processing for tabular information extraction is highly
desired in many organizations, from industry to government. Prior works have
addressed this problem under table detection and table structure detection
tasks. Proposed solutions leveraging deep learning approaches have been giving
promising results in these tasks. However, the impact of dataset structures on
table structure detection has not been investigated. In this study, we provide
a comparison of table structure detection performance with cropped and
uncropped datasets. The cropped set consists of only table images that are
cropped from documents assuming tables are detected perfectly. The uncropped
set consists of regular document images. Experiments show that deep learning
models can improve the detection performance by up to 9% in average precision
and average recall on the cropped versions. Furthermore, the impact of cropped
images is negligible under the Intersection over Union (IoU) values of 50%-70%
when compared to the uncropped versions. However, beyond 70% IoU thresholds,
cropped datasets provide significantly higher detection performance.
