Context-aware STR methods typically use internal autoregressive (AR) language
models (LM). Inherent limitations of AR models motivated two-stage methods
which employ an external LM. The conditional independence of the external LM on
the input image may cause it to erroneously rectify correct predictions,
leading to significant inefficiencies. Our method, PARSeq, learns an ensemble
of internal AR LMs with shared weights using Permutation Language Modeling. It
unifies context-free non-AR and context-aware AR inference, and iterative
refinement using bidirectional context. Using synthetic training data, PARSeq
achieves state-of-the-art (SOTA) results in STR benchmarks (91.9% accuracy) and
more challenging datasets. It establishes new SOTA results (96.0% accuracy)
when trained on real data. PARSeq is optimal on accuracy vs parameter count,
FLOPS, and latency because of its simple, unified structure and parallel token
processing. Due to its extensive use of attention, it is robust on
arbitrarily-oriented text which is common in real-world images. Code,
pretrained weights, and data are available at: this https URL.

PARSeq 使用 Permutation Language Modeling 学习了一组共享权重的内部 AR 语言模型的集合，实现了无上下文的非 AR 和有上下文的 AR 推理以及使用双向上下文的迭代细化，使用注意力机制进行文本识别，取得了在 STR 基准测试中的最新成果，并在一些具有挑战性的数据集上建立了新的 SOTA 结果。