  Scene text recognition is a challenging task due to the complex backgrounds
and diverse variations of text instances. In this paper, we propose a novel
Semantic GAN and Balanced Attention Network (SGBANet) to recognize the texts in
scene images. The proposed method first generates the simple semantic feature
using Semantic GAN and then recognizes the scene text with the Balanced
Attention Module. The Semantic GAN aims to align the semantic feature
distribution between the support domain and target domain. Different from the
conventional image-to-image translation methods that perform at the image
level, the Semantic GAN performs the generation and discrimination on the
semantic level with the Semantic Generator Module (SGM) and Semantic
Discriminator Module (SDM). For target images (scene text images), the Semantic
Generator Module generates simple semantic features that share the same feature
distribution with support images (clear text images). The Semantic
Discriminator Module is used to distinguish the semantic features between the
support domain and target domain. In addition, a Balanced Attention Module is
designed to alleviate the problem of attention drift. The Balanced Attention
Module first learns a balancing parameter based on the visual glimpse vector
and semantic glimpse vector, and then performs the balancing operation for
obtaining a balanced glimpse vector. Experiments on six benchmarks, including
regular datasets, i.e., IIIT5K, SVT, ICDAR2013, and irregular datasets, i.e.,
ICDAR2015, SVTP, CUTE80, validate the effectiveness of our proposed method.
