TDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignment

📰 ArXiv cs.AI

TDATR improves end-to-end table recognition via table detail-aware learning and cell-level visual alignment

advanced Published 25 Mar 2026

Action Steps

Propose TDATR, a table detail-aware table recognition approach
Implement table detail-aware learning to capture table structure and content
Apply cell-level visual alignment to improve recognition accuracy
Evaluate TDATR on benchmark datasets to demonstrate its effectiveness

Who Needs to Know This

Data scientists and AI engineers working on document analysis tasks can benefit from TDATR as it improves table recognition accuracy and simplifies workflows

Key Insight

💡 TDATR simplifies table recognition workflows and improves accuracy in data-constrained scenarios

Key Takeaways

TDATR improves end-to-end table recognition via table detail-aware learning and cell-level visual alignment

Full Article

Title: TDATR: Improving End-to-End Table Recognition via Table Detail-Aware Learning and Cell-Level Visual Alignment

Abstract:
arXiv:2603.22819v1 Announce Type: cross Abstract: Tables are pervasive in diverse documents, making table recognition (TR) a fundamental task in document analysis. Existing modular TR pipelines separately model table structure and content, leading to suboptimal integration and complex workflows. End-to-end approaches rely heavily on large-scale TR data and struggle in data-constrained scenarios. To address these issues, we propose TDATR (Table Detail-Aware Table Recognition) improves end-to-end

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