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arXiv Computer Vision By Genpei Zhang

Can Scene Text Recognition Read Rare Compositions?

Read the original on arXiv Computer Vision →

The paper reports that scene text recognition models, while achieving 89–97% accuracy on standard benchmarks, perform significantly worse on rare word–trigram combinations, with a 10–18 point drop in accuracy at the rare‑word/rare‑trigram corner across multiple languages and models. Scaling the vision backbone improves overall accuracy but does not alleviate this corner‑specific deficit. The authors identify the autoregressive decoder’s lexical prior as the root cause and show that architectural changes—specifically moving from autoregressive to CTC decoding—yield the largest improvement for these rare compositions.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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