First adaptation of Uncertainty Herding to cascaded TD-TSR pipelines via RankFusion and CAPA extensions yields consistent gains over baselines on four table extraction datasets under annotation budgets of 71-500 documents.
In: International Conference on Document Analysis and Recognition (IC- DAR) (2025)
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ConRTF adds an edge-constrained fine-grained localization loss to a distribution-based real-time detector to improve boundary accuracy in table structure recognition, claiming up to +1.6 GriTS gains on PubTables-1M while remaining data-efficient.
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Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines
First adaptation of Uncertainty Herding to cascaded TD-TSR pipelines via RankFusion and CAPA extensions yields consistent gains over baselines on four table extraction datasets under annotation budgets of 71-500 documents.
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ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition
ConRTF adds an edge-constrained fine-grained localization loss to a distribution-based real-time detector to improve boundary accuracy in table structure recognition, claiming up to +1.6 GriTS gains on PubTables-1M while remaining data-efficient.