Revisiting Structural Dependency in Autoregressive Multi-Task Table Recognition via Order-Independent Cell-Level Representations
arXiv:2606.17874v1 Announce Type: cross Abstract: Multi-task table recognition jointly addresses table structure prediction, cell localization, and cell content recognition within a unified framework. Existing approaches often rely on autoregressive decoders to generate table structures and reuse their hidden states for cell localization and content recognition. This autoregressive generation process can make cell representations order-dependent, degrading global consistency across cells. This paper proposes a structural refinement module that produces order-independent cell features through n
The continuous drive for more robust and efficient AI models in computer vision and natural language processing is leading to ongoing research in optimizing core architectural components.
Improving multi-task table recognition is critical for automating data extraction from documents, impacting various industries that rely on unstructured and semi-structured data.
This research suggests a potential improvement in the accuracy and consistency of AI systems designed to interpret complex table structures, reducing errors stemming from processing order dependencies.
- · AI researchers
- · Data extraction software companies
- · Financial services
- · Healthcare administration
- · Companies reliant on manual data entry
- · Legacy OCR solutions
More accurate and reliable automated data processing from documents will reduce operational costs for businesses.
Enhanced table understanding could accelerate the development of advanced AI agents capable of deeper document comprehension.
Improved data accessibility could lead to new analytical insights and business models, particularly in regulated industries.
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Read at arXiv cs.LG