Active Learning for Cascaded Object Detection: Balancing Coverage and Uncertainty in Table Extraction Pipelines
arXiv:2607.00747v1 Announce Type: cross Abstract: Table extraction from business documents relies on a cascaded pipeline where Table Detection (TD) first localizes tables and Table Structure Recognition (TSR) then recovers their internal layout. Building task-specific training sets for this pipeline is costly, particularly for TSR which requires fine-grained structural annotations. Active learning (AL) can reduce this annotation burden, yet most AL strategies are designed for single-model tasks and do not account for inter-stage dependencies in cascaded architectures. In this work, we present
The proliferation of digital documents and the increasing demand for efficient data extraction drives continuous innovation in AI-powered automation and active learning techniques.
This development can significantly reduce the cost and time associated with training data creation for complex, cascaded AI systems, making advanced document processing more accessible and scalable.
The proposed active learning strategy specifically addresses the inter-stage dependencies in multi-model AI pipelines, leading to more efficient annotation processes and potentially higher accuracy in complex tasks like table extraction.
- · AI researchers and data scientists
- · Companies processing large volumes of documents
- · Industries reliant on data extraction (e.g., finance, legal)
- · Document AI software providers
- · Manual data entry services (over time)
More cost-effective and accurate automation of document-based workflows will become possible.
This efficiency gain could accelerate the adoption of AI in sectors previously constrained by data annotation costs and complexity.
The methodology might be adapted to other cascaded AI tasks beyond document processing, fostering a new generation of active learning systems for complex pipelines.
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Read at arXiv cs.AI