Lightweight and Production-Ready PDF Visual Element Parsing
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...
| Authors | Meizhu Liu, Yassi Abbasi, Matthew Rowe et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 April 2026 |
| Last updated at source | 25 April 2026 |
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