TableBank: Benchmark for Image-based Table Detection and Recognition

A collaboration between researchers from China's Beihang University and Microsoft Research Asia has produced TableBank, a new image-based dataset for table detection and recognition built with novel weak supervision from Word and Latex documents on the Internet. Researchers built several strong baselines using SOTA models with deep neural networks, which will enable deployment of more deep learning methods to table detection and recognition tasks. TableBank has been open-sourced on Github. "Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousands human labeled examples, which is difficult to generalize on real world applications. With TableBank that contains 417K high-quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks."