Lightweight PDF Parser Achieves 96% Visual Element Detection Accuracy
A new lightweight PDF parsing framework achieves over 96% accuracy in detecting visual elements like figures, tables, and forms, and 93% accuracy in caption association. Developed for multimodal retrieval-augmented generation (RAG), the system uses spatial heuristics, layout analysis, and semantic similarity to overcome limitations of existing parsers, which often miss complex visuals, extract non-informative artifacts, produce fragmented elements, and fail to reliably associate captions. The framework significantly outperforms previous methods on benchmark datasets and internal product data, enhancing downstream retrieval and question answering. The paper is available on arXiv under identifier 2604.23276.
Key facts
- The framework achieves ≥96% visual element detection accuracy.
- Caption association accuracy is 93%.
- It uses spatial heuristics, layout analysis, and semantic similarity.
- Existing parsers often miss complex visuals and extract artifacts like watermarks and logos.
- The solution is designed for multimodal retrieval-augmented generation (RAG).
- It outperforms previous methods on benchmark datasets and internal product data.
- The paper is published on arXiv with identifier 2604.23276.
- The framework is lightweight and production-ready.
Entities
Institutions
- arXiv