TextVerifier: Seeing Beyond Recognition to Verify Chinese Text Rendering

Text‑to‑image models increasingly generate readable Chinese text that still contains local rendering defects, such as a character with an extra or missing stroke or a malformed component. Such errors are invisible to OCR and vision–language models, which are trained for recognition and are therefore invariant to the very structural deviations that define a rendering error. We formalize text rendering verification as a target‑conditioned problem: given a target character and an image crop, decide whether the crop is a correct rendering of that character. Our verifier TEXTVERIFIER feeds the text tower a character’s Ideographic Description Sequence (IDS) rather than a single‑character symbol, and is trained on 12.7M crop–IDS pairs plus label‑free “same‑character, different‑quality” hard pairs sampled from the training trajectory of a text‑region inpainting model. To evaluate this ability we build TEXTRENDERBENCH‑ZH, the first per‑character benchmark for generated Chinese text, annotating position, identity, and correctness for 7,358 characters. TEXTVERIFIER reaches an F1 of 0.844, far above OCR confidence (0.526), a general VLM (0.329), and TextPecker (0.590). Used as a per‑character reward for Flow‑GRPO post‑training, it further reduces rendering errors, showing that its score can both detect and correct errors.