  Scene text spotting aims to detect and recognize the entire word or sentence
with multiple characters in natural images. It is still challenging because
ambiguity often occurs when the spacing between characters is large or the
characters are evenly spread in multiple rows and columns, making many visually
plausible groupings of the characters (e.g. "BERLIN" is incorrectly detected as
"BERL" and "IN" in Fig. 1(c)). Unlike previous works that merely employed
visual features for text detection, this work proposes a novel text spotter,
named Ambiguity Eliminating Text Spotter (AE TextSpotter), which learns both
visual and linguistic features to significantly reduce ambiguity in text
detection. The proposed AE TextSpotter has three important benefits. 1) The
linguistic representation is learned together with the visual representation in
a framework. To our knowledge, it is the first time to improve text detection
by using a language model. 2) A carefully designed language module is utilized
to reduce the detection confidence of incorrect text lines, making them easily
pruned in the detection stage. 3) Extensive experiments show that AE
TextSpotter outperforms other state-of-the-art methods by a large margin. For
example, we carefully select a validation set of extremely ambiguous samples
from the IC19-ReCTS dataset, where our approach surpasses other methods by more
than 4%. The code has been released at
https://github.com/whai362/AE_TextSpotter. The image list and evaluation
scripts of the validation set have been released at
https://github.com/whai362/TDA-ReCTS.
