This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text (RRC-ArT) that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting.
Scene text recognition has attracted particular research interest because it is a very challenging problem and has various applications.
Moreover, we further investigate the recognition module of our method separately, which significantly outperforms state-of-the-art methods on both regular and irregular text datasets for scene text recognition.
Reading text in the wild is a very challenging task due to the diversity of text instances and the complexity of natural scenes.
An innovative rectification network is developed, where a line-fitting transformation is designed to estimate the pose of text lines in scenes.
Scene text recognition has recently been widely treated as a sequence-to-sequence prediction problem, where traditional fully-connected-LSTM (FC-LSTM) has played a critical role.
In this work, we propose a simple yet robust approach for scene text recognition.