  The diversity of tables makes table detection a great challenge, leading to
existing models becoming more tedious and complex. Despite achieving high
performance, they often overfit to the table style in training set, and suffer
from significant performance degradation when encountering out-of-distribution
tables in other domains. To tackle this problem, we start from the essence of
the table, which is a set of text arranged in rows and columns. Based on this,
we propose a novel, light-weighted and robust Table Detection method based on
Learning Text Arrangement, namely TDeLTA. TDeLTA takes the text blocks as
input, and then models the arrangement of them with a sequential encoder and an
attention module. To locate the tables precisely, we design a
text-classification task, classifying the text blocks into 4 categories
according to their semantic roles in the tables. Experiments are conducted on
both the text blocks parsed from PDF and extracted by open-source OCR tools,
respectively. Compared to several state-of-the-art methods, TDeLTA achieves
competitive results with only 3.1M model parameters on the large-scale public
datasets. Moreover, when faced with the cross-domain data under the 0-shot
setting, TDeLTA outperforms baselines by a large margin of nearly 7%, which
shows the strong robustness and transferability of the proposed model.
