arXiv:2607.07179v1 Announce Type: cross Abstract: Document Visual Question Answering (DocVQA) presents a complex multimodal challenge, requiring models to exploit visual, textual, and layout information from documents. Although Vision-Language Models (VLMs) have shown remarkable performance in text-vision tasks, their robustness and transferability to different document domains remains underexplored. In this study, we present a comprehensive evaluation of 8 open-source pretrained VLMs on DocVQA in three different document domains: industrial documents of varying type, infographics, and present
This study emerges as Vision-Language Models (VLMs) proliferate and their practical application in diverse document understanding tasks becomes a critical area for evaluation and development.
It provides a crucial comparative benchmark for VLM performance in complex Document Visual Question Answering (DocVQA), highlighting the robustness and transferability across different document domains, which is vital for enterprise VLM adoption.
The understanding of which open-source VLMs are most effective and transferable for DocVQA in industrial and infographic contexts is greatly enhanced, informing future model selection and domain adaptation strategies.
- · VLM developers
- · Enterprises using DocVQA
- · AI researchers in CV/LG
- · Open-source AI community
- · Proprietary VLM vendors with weak domain adaptation
- · Companies relying on outdated document processing models
Improved performance and broader deployment of VLMs for automated document processing tasks across various industries.
Increased competition and specialization among VLM developers to create models with superior domain-specific adaptability and transferability.
Automation of highly complex knowledge work currently reliant on manual interpretation of disparate document types, leading to efficiency gains and workforce restructuring.
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Read at arXiv cs.LG