🤖 AI Summary
This work addresses the lack of intrinsic validation mechanisms for extraction fidelity in existing intelligent document processing (IDP) systems, which often leads to silent error propagation. The authors propose “Reconstruction as Verification” (RaV-IDP), a novel framework that introduces reconstruction as a core component of the document processing pipeline. Specifically, a dedicated reconstructor renders extracted entities back into visual form and compares them against the original document regions to produce an unsupervised fidelity score. The approach integrates a vision-enhanced GPT-4.1 fallback strategy, a fidelity-threshold-triggered verification mechanism, and a multi-stage evaluation benchmark to enable quantifiable assessment of extraction quality. Experimental results demonstrate that RaV-IDP effectively filters low-quality outputs and significantly enhances the robustness of downstream tasks. The code is publicly released.
📝 Abstract
Intelligent document processing pipelines extract structured entities (tables, images, and text) from documents for use in downstream systems such as knowledge bases, retrieval-augmented generation, and analytics. A persistent limitation of existing pipelines is that extraction output is produced without any intrinsic mechanism to verify whether it faithfully represents the source. Model-internal confidence scores measure inference certainty, not correspondence to the document, and extraction errors pass silently into downstream consumers.
We present Reconstruction as Validation (RaV-IDP), a document processing pipeline that introduces reconstruction as a first-class architectural component. After each entity is extracted, a dedicated reconstructor renders the extracted representation back into a form comparable to the original document region, and a comparator scores fidelity between the reconstruction and the unmodified source crop. This fidelity score is a grounded, label-free quality signal. When fidelity falls below a per-entity-type threshold, a structured GPT-4.1 vision fallback is triggered and the validation loop repeats. We enforce a bootstrap constraint: the comparator always anchors against the original document region, never against the extraction, preventing the validation from becoming circular.
We further propose a per-stage evaluation framework pairing each pipeline component with an appropriate benchmark. The code pipeline is publicly available at https://github.com/pritesh-2711/RaV-IDP for experimentation and use.