ConRTF: Edge-Constrained Boundary Distribution Refinement for Realtime TransFormer Table Structure Recognition
arXiv:2607.00734v1 Announce Type: cross Abstract: Table Structure Recognition (TSR) aims to recover the row and column layout of tables from document images, a key step in document understanding pipelines. Accurate TSR depends on precise boundary localization: small errors in row or column boundaries can propagate into incorrect cell assignments and structural inconsistencies. Yet detection-based approaches treat table elements as generic objects, ignoring a fundamental property of table layout: rows and columns play structurally distinct roles and their boundaries carry unequal importance. We
The continuous advancements in AI and computer vision, especially with Transformer models, are driving more sophisticated document understanding solutions. The need for real-time and accurate table structure recognition is increasing with the growth of digitized documents across various industries.
Improved table structure recognition directly impacts the efficiency and accuracy of data extraction from documents, which is crucial for business process automation, analytics, and intelligent document processing systems. This technology helps unlock structured data from unstructured or semi-structured document images.
This research proposes a new method that could lead to more robust and accurate table structure recognition, particularly for real-time applications and in scenarios where precise boundary localization is critical. It moves beyond generic object detection to specifically address the nuanced roles of row and column boundaries.
- · Document AI platforms
- · Enterprise software vendors
- · Data analytics companies
- · Financial services
- · Manual data entry services
- · Less accurate OCR solutions
- · Traditional rule-based data extraction methods
Higher accuracy in document parsing and automated data extraction from tables will improve operational efficiency across many sectors.
Enhanced data quality from digitized documents could fuel more robust business intelligence and AI model training.
The reduced cost and increased reliability of data extraction might accelerate the digitization of legacy documents and processes in organizations.
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Read at arXiv cs.AI