Scene text recognition systems must decide what to do when an image contains no reliably readable text. We compare two abstention contracts for a vision-language scene-text recognizer. Under implicit refusal, the model signals unreadability inside its own transcription output. Under explicit state gating, a separate five-way state classifier decides whether a transcription is emitted at all. Across four training variants sharing the same backbone, direct OCR yields similar accuracy on readable text; the two contracts differ mainly in how they handle inputs with no readable text. Gating reduces false transcription (emitting text where none is readable) from 45.9% to 1.7%, at the cost of falsely abstaining on 3.8% of readable inputs. Which contract is preferable, however, cannot be determined without first specifying how much a correct abstention is worth. When rejected inputs are excluded from scoring, all gated configurations show similar risk; when a correct abstention is credited as a useful outcome, gating lowers risk on the diagnostic set. We present this preliminary analysis not as a context-free model ranking, but as a framework for choosing an abstention contract at deployment time. These results motivate future systems that use the predicted state to select a recovery action before abstaining.