TableBank: A Benchmark Dataset for Table Detection and Recognition
원문 초록
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 and models are available at \url{https://github.com/doc-analysis/TableBank}.
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UniHDSA: A Unified Relation Prediction Approach for Hierarchical Document Structure Analysis
TabPedia: Towards Comprehensive Visual Table Understanding with Concept Synergy
ClusterTabNet: Supervised clustering method for table detection and table structure recognition
Table Detection for Visually Rich Document Images
A large-scale dataset for end-to-end table recognition in the wild
Aligning benchmark datasets for table structure recognition
토론
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