3 papers
cs.CV2024
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretraining
ShengYun Peng, Aishwarya Chakravarthy, Seongmin Lee +3
Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse. Prior work on table recognition (TR) has m…
cs.CV2024
Self-Supervised Pre-Training for Table Structure Recognition Transformer
ShengYun Peng, Seongmin Lee, Xiaojing Wang +2
Table structure recognition (TSR) aims to convert tabular images into a machine-readable format. Although hybrid convolutional neural network (CNN)-transformer architecture is wide…
cs.CV2023
High-Performance Transformers for Table Structure Recognition Need Early Convolutions
ShengYun Peng, Seongmin Lee, Xiaojing Wang +2
Table structure recognition (TSR) aims to convert tabular images into a machine-readable format, where a visual encoder extracts image features and a textual decoder generates tabl…