LISTER: Neighbor Decoding for Length-Insensitive Scene Text Recognition
Changxu Cheng Peng Wang Cheng Da Qi Zheng Cong Yao
The diversity in length constitutes a significant characteristic of text. Due
to the long-tail distribution of text lengths, most existing methods for scene
text recognition (STR) only work well on short or seen-length text, lacking the
capability of recognizing longer text or performing length extrapolation. This
is a crucial issue, since the lengths of the text to be recognized are usually
not given in advance in real-world applications, but it has not been adequately
investigated in previous works. Therefore, we propose in this paper a method
called Length-Insensitive Scene TExt Recognizer (LISTER), which remedies the
limitation regarding the robustness to various text lengths. Specifically, a
Neighbor Decoder is proposed to obtain accurate character attention maps with
the assistance of a novel neighbor matrix regardless of the text lengths.
Besides, a Feature Enhancement Module is devised to model the long-range
dependency with low computation cost, which is able to perform iterations with
the neighbor decoder to enhance the feature map progressively. To the best of
our knowledge, we are the first to achieve effective length-insensitive scene
text recognition. Extensive experiments demonstrate that the proposed LISTER
algorithm exhibits obvious superiority on long text recognition and the ability
for length extrapolation, while comparing favourably with the previous
state-of-the-art methods on standard benchmarks for STR (mainly short text).