Lightweight and Production-Ready PDF Visual Element Parsing

• URL: http://arxiv.org/abs/2604.23276v1
• Date: Sat, 25 Apr 2026 12:40:13 GMT
• Title: Lightweight and Production-Ready PDF Visual Element Parsing
• Abstract summary: Existing PDFs often miss complex visuals, extract non-informative artifacts, produce fragmented elements, and fail to reliably associate captions with their corresponding elements.<n>We present a lightweight and production level PDF parsing framework that can accurately detect visual elements and associates captions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: PDF documents contain critical visual elements such as figures, tables, and forms whose accurate extraction is essential for document understanding and multimodal retrieval-augmented generation (RAG). Existing PDF parsers often miss complex visuals, extract non-informative artifacts (e.g., watermarks, logos), produce fragmented elements, and fail to reliably associate captions with their corresponding elements, which degrades downstream retrieval and question answering. We present a lightweight and production level PDF parsing framework that can accurately detect visual elements and associates captions using a combination of spatial heuristics, layout analysis, and semantic similarity. On popular benchmark datasets and internal product data, the proposed solution achieves $\geq96\%$ visual element detection accuracy and $93\%$ caption association accuracy. When used as a preprocessing step for multimodal RAG, it significantly outperforms state-of-the-art parsers and large vision-language models on both internal data and the MMDocRAG benchmark, while reducing latency by over $2\times$. We have deployed the proposed system in challenging production environment.

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