  Scene Text Recognition (STR) methods have demonstrated robust performance in
word-level text recognition. However, in applications the text image is
sometimes long due to detected with multiple horizontal words. It triggers the
requirement to build long text recognition models from readily available short
word-level text datasets, which has been less studied previously. In this
paper, we term this the Out of Length (OOL) text recognition. We establish a
new Long Text Benchmark (LTB) to facilitate the assessment of different methods
in long text recognition. Meanwhile, we propose a novel method called OOL Text
Recognition with sub-String Matching (SMTR). SMTR comprises two
cross-attention-based modules: one encodes a sub-string containing multiple
characters into next and previous queries, and the other employs the queries to
attend to the image features, matching the sub-string and simultaneously
recognizing its next and previous character. SMTR can recognize text of
arbitrary length by iterating the process above. To avoid being trapped in
recognizing highly similar sub-strings, we introduce a regularization training
to compel SMTR to effectively discover subtle differences between similar
sub-strings for precise matching. In addition, we propose an inference
augmentation to alleviate confusion caused by identical sub-strings and improve
the overall recognition efficiency. Extensive experimental results reveal that
SMTR, even when trained exclusively on short text, outperforms existing methods
in public short text benchmarks and exhibits a clear advantage on LTB. Code:
\url{https://github.com/Topdu/OpenOCR}.
