Textural or Textual: How Vision-Language Models Read Text in Images

Hanzhang Wang, Qingyuan Ma
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:63882-63896, 2025.

Abstract

Typographic attacks are often attributed to the ability of multimodal pre-trained models to fuse textual semantics into visual representations, yet the mechanisms and locus of such interference remain unclear. We examine whether such models genuinely encode textual semantics or primarily rely on texture-based visual features. To disentangle orthographic form from meaning, we introduce the ToT dataset, which includes controlled word pairs that either share semantics with distinct appearances (synonyms) or share appearance with differing semantics (paronyms). A layer-wise analysis of Intrinsic Dimension (ID) reveals that early layers exhibit competing dynamics between orthographic and semantic representations. In later layers, semantic accuracy increases as ID decreases, but this improvement largely stems from orthographic disambiguation. Notably, clear semantic differentiation emerges only in the final block, challenging the common assumption that semantic understanding is progressively constructed across depth. These findings reveal how current vision-language models construct text representations through texture-dependent processes, prompting a reconsideration of the gap between visual perception and semantic understanding. The code is available at: https://github.com/Ovsia/Textural-or-Textual

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-wang25bw, title = {Textural or Textual: How Vision-Language Models Read Text in Images}, author = {Wang, Hanzhang and Ma, Qingyuan}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {63882--63896}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/wang25bw/wang25bw.pdf}, url = {https://proceedings.mlr.press/v267/wang25bw.html}, abstract = {Typographic attacks are often attributed to the ability of multimodal pre-trained models to fuse textual semantics into visual representations, yet the mechanisms and locus of such interference remain unclear. We examine whether such models genuinely encode textual semantics or primarily rely on texture-based visual features. To disentangle orthographic form from meaning, we introduce the ToT dataset, which includes controlled word pairs that either share semantics with distinct appearances (synonyms) or share appearance with differing semantics (paronyms). A layer-wise analysis of Intrinsic Dimension (ID) reveals that early layers exhibit competing dynamics between orthographic and semantic representations. In later layers, semantic accuracy increases as ID decreases, but this improvement largely stems from orthographic disambiguation. Notably, clear semantic differentiation emerges only in the final block, challenging the common assumption that semantic understanding is progressively constructed across depth. These findings reveal how current vision-language models construct text representations through texture-dependent processes, prompting a reconsideration of the gap between visual perception and semantic understanding. The code is available at: https://github.com/Ovsia/Textural-or-Textual} }
Endnote
%0 Conference Paper %T Textural or Textual: How Vision-Language Models Read Text in Images %A Hanzhang Wang %A Qingyuan Ma %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-wang25bw %I PMLR %P 63882--63896 %U https://proceedings.mlr.press/v267/wang25bw.html %V 267 %X Typographic attacks are often attributed to the ability of multimodal pre-trained models to fuse textual semantics into visual representations, yet the mechanisms and locus of such interference remain unclear. We examine whether such models genuinely encode textual semantics or primarily rely on texture-based visual features. To disentangle orthographic form from meaning, we introduce the ToT dataset, which includes controlled word pairs that either share semantics with distinct appearances (synonyms) or share appearance with differing semantics (paronyms). A layer-wise analysis of Intrinsic Dimension (ID) reveals that early layers exhibit competing dynamics between orthographic and semantic representations. In later layers, semantic accuracy increases as ID decreases, but this improvement largely stems from orthographic disambiguation. Notably, clear semantic differentiation emerges only in the final block, challenging the common assumption that semantic understanding is progressively constructed across depth. These findings reveal how current vision-language models construct text representations through texture-dependent processes, prompting a reconsideration of the gap between visual perception and semantic understanding. The code is available at: https://github.com/Ovsia/Textural-or-Textual
APA
Wang, H. & Ma, Q.. (2025). Textural or Textual: How Vision-Language Models Read Text in Images. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:63882-63896 Available from https://proceedings.mlr.press/v267/wang25bw.html.

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