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Found 3 papers, 3 papers with code
UniTable: Towards a Unified Framework for Table Structure Recognition via Self-Supervised Pretraining
Tables convey factual and quantitative data with implicit conventions created by humans that are often challenging for machines to parse.
Self-Supervised Pre-Training for Table Structure Recognition Transformer
We discover that the performance gap between the linear projection transformer and the hybrid CNN-transformer can be mitigated by SSP of the visual encoder in the TSR model.
High-Performance Transformers for Table Structure Recognition Need Early Convolutions
This allows it to "see" an appropriate portion of the table and "store" the complex table structure within sufficient context length for the subsequent transformer.
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