Table structure recognition aims to parse tables in unstructured data into
machine-understandable formats. Recent methods address this problem through a
two-stage process or optimized one-stage approaches. Cependant, these methods
either require multiple networks to be serially trained and perform more
time-consuming sequential decoding, or rely on complex post-processing
algorithms to parse the logical structure of tables. They struggle to balance
cross-scenario adaptability, robustness, and computational efficiency. In this
paper, we propose a one-stage end-to-end table structure parsing network called
TableCenterNet. This network unifies the prediction of table spatial and
logical structure into a parallel regression task for the first time, et
implicitly learns the spatial-logical location mapping laws of cells through a
synergistic architecture of shared feature extraction layers and task-specific
decoding. Compared with two-stage methods, our method is easier to train and
faster to infer. Experiments on benchmark datasets show that TableCenterNet can
effectively parse table structures in diverse scenarios and achieve
state-of-the-art performance on the TableGraph-24k dataset. Code is available
at https://github.com/dreamy-xay/TableCenterNet.

Cet article explore les excursions dans le temps et leurs implications.

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