Recognizing irregular text in natural scene images is challenging due to thelarge variance in text appearance, such as curvature, orientation anddistortion. Most existing approaches rely heavily on sophisticated modeldesigns and/or extra fine-grained annotations, which, to some extent, increasethe difficulty in algorithm implementation and data collection. In this work,we propose an easy-to-implement strong baseline for irregular scene textrecognition, using off-the-shelf neural network components and only word-levelannotations. It is composed of a $31$31-layer ResNet, an LSTM-basedencoder-decoder framework and a 2-dimensional attention module. Despite itssimplicity, the proposed method is robust and achieves state-of-the-artperformance on both regular and irregular scene text recognition benchmarks.Code is available at: https://tinyurl.com/ShowAttendRead