Efficient table border segmentation with asymmetric convolutions
Mohammad Minouei, Mohammad Reza Soheili, Didier Stricker · 2022
Automatic table understanding in document images is one of the most challenging topics in the research community. This is owing to the fact that tables may appear in various structures and designs. However, a big majority of tables are designed with ruling lines. Recognizing these lines in images is mandatory in numerous table understanding processes. Previous works have utilized hand-crafted features, merely applicable to distortion-free images. We present a compact CNN as an alternative solution. This method is capable of segmenting the ruling lines in challenging environments. In addition to the proposed architecture, a new dataset is generated for this task that contains 35K labeled samples. The reported results on this dataset show the effectiveness of this method. Our implementation and dataset are available online.