  Scene text recognition (STR) methods have struggled to attain high accuracy
and fast inference speed. Autoregressive (AR)-based models implement the
recognition in a character-by-character manner, showing superiority in accuracy
but with slow inference speed. Alternatively, parallel decoding (PD)-based
models infer all characters in a single decoding pass, offering faster
inference speed but generally worse accuracy. We first present an empirical
study of AR decoding in STR, and discover that the AR decoder not only models
linguistic context, but also provides guidance on visual context perception.
Consequently, we propose Context Perception Parallel Decoder (CPPD) to predict
the character sequence in a PD pass. CPPD devises a character counting module
to infer the occurrence count of each character, and a character ordering
module to deduce the content-free reading order and placeholders. Meanwhile,
the character prediction task associates the placeholders with characters. They
together build a comprehensive recognition context. We construct a series of
CPPD models and also plug the proposed modules into existing STR decoders.
Experiments on both English and Chinese benchmarks demonstrate that the CPPD
models achieve highly competitive accuracy while running approximately 8x
faster than their AR-based counterparts. Moreover, the plugged models achieve
significant accuracy improvements. Code is at
\href{https://github.com/PaddlePaddle/PaddleOCR/blob/dygraph/doc/doc_en/algorithm_rec_cppd_en.md}{this
https URL}.
