# Multi-Modal Vision vs. Text-Based Parsing: Benchmarking LLM Strategies for Invoice Processing

arXiv: https://arxiv.org/abs/2509.04469 · HTML: https://mlsanity.com/paper/2509.04469/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 16 of its 23 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Builds on (most cited first, 16 of 16)

- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://mlsanity.com/paper/2204.08387/) (2022)
- [OCR-free Document Understanding Transformer](https://mlsanity.com/paper/2111.15664/) (2021)
- [MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations](https://mlsanity.com/paper/2407.01523/) (2024)
- [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://mlsanity.com/paper/2202.13669/) (2022)
- [SmolDocling: An ultra-compact vision-language model for end-to-end multi-modal document conversion](https://mlsanity.com/paper/2503.11576/) (2025)
- [DOCBENCH: A Benchmark for Evaluating LLM-based Document Reading Systems](https://mlsanity.com/paper/2407.10701/) (2024)
- [M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework](https://mlsanity.com/paper/2411.06176/) (2024)
- [Docling Technical Report](https://mlsanity.com/paper/2408.09869/) (2024)
- [A Survey of Deep Learning Approaches for OCR and Document Understanding](https://mlsanity.com/paper/2011.13534/) (2020)
- [Deep Learning based Visually Rich Document Content Understanding: A Survey](https://mlsanity.com/paper/2408.01287/) (2024)
- [WONDERBREAD: A Benchmark for Evaluating Multimodal Foundation Models on Business Process Management Tasks](https://mlsanity.com/paper/2406.13264/) (2024)
- [Field Extraction from Forms with Unlabeled Data](https://mlsanity.com/paper/2110.04282/) (2021)
- [Advancing Risk and Quality Assurance: A RAG Chatbot for Improved Regulatory Compliance](https://mlsanity.com/paper/2507.16711/) (2025)
- [Memory-Augmented Agent Training for Business Document Understanding](https://mlsanity.com/paper/2412.15274/) (2024)

## Built on by (most cited first, 1 of 1)

- [Text, Pixels, or Both? Evaluating Input Representations for Multimodal Document QA](https://mlsanity.com/paper/2609.22628/) (2026)

## Abstract (arXiv)

This paper benchmarks eight multi-modal large language models from three families (GPT-5, Gemini 2.5, and open-source Gemma 3) on three diverse openly available invoice document datasets using zero-shot prompting. We compare two processing strategies: direct image processing using multi-modal capabilities and a structured parsing approach converting documents to markdown first. Results show native image processing generally outperforms structured approaches, with performance varying across model types and document characteristics. This benchmark provides insights for selecting appropriate models and processing strategies for automated document systems. Our code is available online.