Multi-Modal Vision vs. Text-Based Parsing: Benchmarking {LLM} Strategies for Invoice Processing
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.
- Veröffentlicht in:
2025 IEEE International Conference on Big Data (BigData) - Typ:
Inproceedings - Autoren:
- Jahr:
2025 - Source:
http://arxiv.org/abs/2509.04469
Informationen zur Zitierung
: Multi-Modal Vision vs. Text-Based Parsing: Benchmarking {LLM} Strategies for Invoice Processing, 2025 IEEE International Conference on Big Data (BigData), 2025, {arXiv}:2509.04469, August, {arXiv}, http://arxiv.org/abs/2509.04469, Berghaus.etal.2025b,
@Inproceedings{Berghaus.etal.2025b,
author={Berghaus, David; Berger, Armin; Hillebrand, Lars; Cvejoski, Kostadin; Sifa, Rafet},
title={Multi-Modal Vision vs. Text-Based Parsing: Benchmarking {LLM} Strategies for Invoice Processing},
booktitle={2025 IEEE International Conference on Big Data (BigData)},
number={{arXiv}:2509.04469},
month={August},
publisher={{arXiv}},
url={http://arxiv.org/abs/2509.04469},
year={2025},
abstract={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...}}
