  Recent state-of-the-art scene text recognition methods have primarily focused
on horizontal text in images. However, in several Asian countries, including
China, large amounts of text in signs, books, and TV commercials are vertically
directed. Because the horizontal and vertical texts exhibit different
characteristics, developing an algorithm that can simultaneously recognize both
types of text in real environments is necessary. To address this problem, we
adopted the direction encoding mask (DEM) and selective attention network (SAN)
methods based on supervised learning. DEM contains directional information to
compensate in cases that lack text direction; therefore, our network is trained
using this information to handle the vertical text. The SAN method is designed
to work individually for both types of text. To train the network to recognize
both types of text and to evaluate the effectiveness of the designed model, we
prepared a new synthetic vertical text dataset and collected an actual vertical
text dataset (VTD142) from the Web. Using these datasets, we proved that our
proposed model can accurately recognize both vertical and horizontal text and
can achieve state-of-the-art results in experiments using benchmark datasets,
including the street view test (SVT), IIIT-5k, and ICDAR. Although our model is
relatively simple as compared to its predecessors, it maintains the accuracy
and is trained in an end-to-end manner.
