Scene text recognition (STR) methods have struggled to attain high accuracy
and fast inference speed. Autoregressive (AR)-based STR model uses the
previously recognized characters to decode the next character iteratively. It
shows superiority in terms of accuracy. However, the inference speed is slow
also due to this iteration. Alternatively, parallel decoding (PD)-based STR
model infers all the characters in a single decoding pass. It has advantages in
terms of inference speed but worse accuracy, as it is difficult to build a
robust recognition context in such a pass. In this paper, we first present an
empirical study of AR decoding in STR. In addition to constructing a new AR
model with the top accuracy, we find out that the success of AR decoder lies
also in providing guidance on visual context perception rather than language
modeling as claimed in existing studies. As a consequence, we propose Context
Perception Parallel Decoder (CPPD) to decode the character sequence in a single
PD pass. CPPD devises a character counting module and a character ordering
module. Given a text instance, the former infers the occurrence count of each
character, while the latter deduces the character reading order and
placeholders. Together with the character prediction task, they construct a
context that robustly tells what the character sequence is and where the
characters appear, well mimicking the context conveyed by AR decoding.
Experiments on both English and Chinese benchmarks demonstrate that CPPD models
achieve highly competitive accuracy. Moreover, they run approximately 7x faster
than their AR counterparts, and are also among the fastest recognizers. The
code will be released soon.

场景文本识别方法一直在追求高准确率和快速推断速度。本文通过经验研究发现，自回归解码在提供视觉上下文感知方面比语言建模更有效。因此，提出了上下文感知并行解码器（CPPD），它在单次解码中构建了一个稳健的上下文，使得其准确率和推断速度较传统方法显著提高。