See the Text: From Tokenization to Visual Reading
People see text. Humans read by recognizing words as visual objects,
including their shapes, layouts, and patterns, before connecting them to
meaning, which enables us to handle typos, distorted fonts, and various scripts
effectively. Modern large language models (LLMs), however, rely on subword
tokenization, fragmenting text into pieces from a fixed vocabulary. While
effective for high-resource languages, this approach over-segments low-resource
languages, yielding long, linguistically meaningless sequences and inflating
computation. In this work, we challenge this entrenched paradigm and move
toward a vision-centric alternative. Our method, SeeTok, renders text as images
(visual-text) and leverages pretrained multimodal LLMs to interpret them,
reusing strong OCR and text-vision alignment abilities learned from large-scale
multimodal training. Across three different language tasks, SeeTok matches or
surpasses subword tokenizers while requiring 4.43 times fewer tokens and
reducing FLOPs by 70.5%, with additional gains in cross-lingual generalization,
robustness to typographic noise, and linguistic hierarchy. SeeTok signals a
shift from symbolic tokenization to human-like visual reading, and takes a step
toward more natural and cognitively inspired language models.
68f9f336b9b2e4ae04673b51
Ling Xing
claimed_verified
2025-10-23T14:44:39.643Z
66ebce4f322dcfe976c30460
/avatars/aafb9e5e639861aa711679d76211a44e.svg
Ling Xing
user
ling441
68f9f336b9b2e4ae04673b52
Alex Jinpeng Wang
68f9f336b9b2e4ae04673b53
Rui Yan
68f9f336b9b2e4ae04673b54
Hongyu Qu
68f9f336b9b2e4ae04673b55
Zechao Li
68f9f336b9b2e4ae04673b56
Jinhui Tang
See the Text: From Tokenization to Visual Reading
People see text. Humans read by recognizing words as visual objects,
including their shapes, layouts, and patterns, before connecting them to
meaning, which enables us to handle typos, distorted fonts, and various scripts
effectively. Modern large language models (LLMs), however, rely on subword
tokenization, fragmenting text into pieces from a fixed vocabulary. While
effective for high-resource languages, this approach over-segments low-resource
languages, yielding long, linguistically meaningless sequences and inflating
computation. In this work, we challenge this entrenched paradigm and move
toward a vision-centric alternative. Our method, SeeTok, renders text as images
(visual-text) and leverages pretrained multimodal LLMs to interpret them,
reusing strong OCR and text-vision alignment abilities learned from large-scale
multimodal training. Across three different language tasks, SeeTok matches or
surpasses subword tokenizers while requiring 4.43 times fewer tokens and
reducing FLOPs by 70.5%, with additional gains in cross-lingual generalization,
robustness to typographic noise, and linguistic hierarchy. SeeTok signals a
shift from symbolic tokenization to human-like visual reading, and takes a step
toward more natural and cognitively inspired language models.
SeeTok, a vision-centric method, renders text as images and uses pretrained multimodal LLMs to interpret them, offering efficiency and robustness improvements over traditional subword tokenization.
subword tokenization SeeTok visual-text pretrained multimodal LLMs OCR text-vision alignment cross-lingual generalization typographic noise linguistic hierarchy