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Boundary localization errors in table structure recognition can be drastically reduced by treating rows and columns with distinct structural priorities, leading to up to +1.6 GriTS points improvement in accuracy.
Active learning can significantly reduce annotation costs in table extraction pipelines, with CAPA emerging as the most reliable strategy for balancing coverage and uncertainty.
