  Natural scene text detection is an important aspect of scene understanding
and could be a useful tool in building engaging augmented reality applications.
In this work, we address the problem of false positives in text spotting. We
propose improving the performace of sliding window text spotters by looking for
character pairs (bigrams) rather than single characters. An efficient
convolutional neural network is designed and trained to detect bigrams. The
proposed detector reduces false positive rate by 28.16% on the ICDAR 2015
dataset. We demonstrate that detecting bigrams is a computationally inexpensive
way to improve sliding window text spotters.
