  Scene text images have different shapes and are subjected to various
distortions, e.g. perspective distortions. To handle these challenges, the
state-of-the-art methods rely on a rectification network, which is connected to
the text recognition network. They form a linear pipeline which uses text
rectification on all input images, even for images that can be recognized
without it. Undoubtedly, the rectification network improves the overall text
recognition performance. However, in some cases, the rectification network
generates unnecessary distortions on images, resulting in incorrect predictions
in images that would have otherwise been correct without it. In order to
alleviate the unnecessary distortions, the portmanteauing of features is
proposed. The portmanteau feature, inspired by the portmanteau word, is a
feature containing information from both the original text image and the
rectified image. To generate the portmanteau feature, a non-linear input
pipeline with a block matrix initialization is presented. In this work, the
transformer is chosen as the recognition network due to its utilization of
attention and inherent parallelism, which can effectively handle the
portmanteau feature. The proposed method is examined on 6 benchmarks and
compared with 13 state-of-the-art methods. The experimental results show that
the proposed method outperforms the state-of-the-art methods on various of the
benchmarks.
