TableBank: Table Benchmark for Image-based Table Detection and Recognition - Li, Minghao; Cui, Lei; Huang, Shaohan; Wei, Furu; Zhou, Ming; Li, Zhoujun (LREC 2020, 2020)
We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand 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. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset can be downloaded from [https://github.com/doc-analysis/TableBank]. [TableBank, table detection and recognition, benchmark, weak supervision dataset built method, image-based deep learning network]