Out-of-Length Scene Text Recognition: A Two-Axis Diagnosis and a Training-Free Fix
Scene Text Recognition (STR) models are trained almost exclusively on word crops of at most 25 characters, yet real deployments (signage, product labels, dense captions) require reading much longer text. This paper diagnoses that failure and then closes it. The diagnosis separates out-of-length failure into two simultaneously extrapolating axes (the encoder's width axis and the decoder's time axis) and shows that encoder width, not decoder length, is the dominant failure mode. Representation-side fixes bring only partial relief: training-free rotary rescalings recover at most 2-4 points of character error rate (CER), and a weighted fine-tuning recipe recovers 6-8 points while improving st...
| Authors | Zobeir Raisi, John Zelek |
|---|---|
| Primary category | cs.CV |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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