arXiv:2602.00122v3 Announce Type: replace-cross Abstract: In recent years, image editing models have made significant progress, enabling users to manipulate visual content in a flexible and interactive manner through natural language instructions. However, an important yet underexplored research direction remains dense visual document image editing, which involves modifying textual content within images while faithfully preserving the original text style and background context. Existing methods primarily focus on English scenarios and images with relatively sparse text, and thus cannot adequat
The proliferation of advanced image editing models, increasingly leveraging natural language, necessitates robust evaluation benchmarks to tackle complex real-world challenges like dense document editing.
Improving the capability of AI models to precisely edit textual content within images has significant implications for automating document processing, enhancing digital content creation, and enabling new forms of human-computer interaction.
This research introduces a crucial benchmark for evaluating the fidelity and effectiveness of image editing models on dense visual documents, moving beyond sparse English text to more complex, multilingual scenarios.
- · AI researchers (image editing)
- · Document automation software
- · Content creation platforms
- · Enterprise AI
- · Manual data entry
- · Inflexible document management systems
More accurate and versatile AI-powered document manipulation tools become available to users.
Reduced human effort and increased efficiency in tasks involving editing and processing visual documents across various industries.
The development of AI systems capable of seamlessly integrating and modifying information across diverse visual and textual formats, blurring the lines between creation and editing.
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Read at arXiv cs.AI