CascadeTabNet is a deep learning approach that simultaneously detects tables and recognizes their structural cells from image-based documents using a single CNN model. The architecture employs Cascade mask R-CNN with HRNet backbone and a cascade mechanism to progressively refine table and cell detection. It was presented as an oral paper at CVPR 2020 and supports end-to-end inference on document images.