Mask TextSpotter: An End-to-End Trainable Neural Network for Spotting Text with Arbitrary Shapes
This addresses the challenge of reading text in the wild for applications like image analysis and OCR, with incremental improvements in handling arbitrary shapes.
The paper tackles the problem of scene text spotting for simultaneous text detection and recognition in natural images, presenting Mask TextSpotter, an end-to-end trainable neural network that achieves superior performance over state-of-the-art methods on multiple datasets, including handling irregular shapes like curved text.
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.