Mask TextSpotter: An End to End Trainable Neural Network for Spotting Text with Arbitrary Shapes
Unifying text detection and text recognition in an end to end training fashion has become a new trend for reading text in the wild, as these two tasks are highly relevant and complementary. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end to end trainable neural network named as Mask TextSpotter is presented. Different from the previous text spotters that follow the pipeline consisting of a proposal generation network and a sequence to sequence recognition network, Mask TextSpotter enjoys a simple and smooth end to end learning procedure, in which both detection and recognition can be achieved directly from two dimensional space via semantic segmentation. Further, a spatial attention module is proposed to enhance the performance and universality. Benefiting from the proposed two dimensional representation on both detection and recognition, it easily handles text instances of irregular shapes, for instance, curved text. We evaluate it on four English datasets and one multi language dataset, achieving consistently superior performance over state of the art methods in both detection and end to end text recognition tasks. 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.
IEEE Transactions on Pattern Analysis and Machine Intelligence