  Candidate text region extraction plays a critical role in convolutional
neural network (CNN) based text detection from natural images. In this paper,
we propose a CNN based scene text detection algorithm with a new text region
extractor. The so called candidate text region extractor I-MSER is based on
Maximally Stable Extremal Region (MSER), which can improve the independency and
completeness of the extracted candidate text regions. Design of I-MSER is
motivated by the observation that text MSERs have high similarity and are close
to each other. The independency of candidate text regions obtained by I-MSER is
guaranteed by selecting the most representative regions from a MSER tree which
is generated according to the spatial overlapping relationship among the MSERs.
A multi-layer CNN model is trained to score the confidence value of the
extracted regions extracted by the I-MSER for text detection. The new text
detection algorithm based on I-MSER is evaluated with wide-used ICDAR 2011 and
2013 datasets and shows improved detection performance compared to the existing
algorithms.
