StrucTab achieves SOTA table parsing performance by unifying structural subtasks through sequential reasoning and using decomposed RL rewards in Uni-TabRL, plus a new TableVerse-5K benchmark.
arXiv:2501.11800 (2025)
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FastTab combines a Tiny Recursive Module and axial 1D Transformer encoders to predict table grids, headers, and cell spans directly, achieving competitive accuracy on four benchmarks with low-latency inference.
TableSeq unifies table structure recognition, content extraction, and cell localization by generating an interleaved autoregressive sequence of HTML tags, cell text, and discretized coordinate tokens from an input image.
citing papers explorer
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StrucTab: A Structured Optimization Framework for Table Parsing
StrucTab achieves SOTA table parsing performance by unifying structural subtasks through sequential reasoning and using decomposed RL rewards in Uni-TabRL, plus a new TableVerse-5K benchmark.
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FastTab: A Fast Table Recognizer with a Tiny Recursive Module and 1D Transformers
FastTab combines a Tiny Recursive Module and axial 1D Transformer encoders to predict table grids, headers, and cell spans directly, achieving competitive accuracy on four benchmarks with low-latency inference.
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TableSeq: Unified Generation of Structure, Content, and Layout
TableSeq unifies table structure recognition, content extraction, and cell localization by generating an interleaved autoregressive sequence of HTML tags, cell text, and discretized coordinate tokens from an input image.