Many approaches have recently been proposed to detect irregular scene text
and achieved promising results. However, their localization results may not
well satisfy the following text recognition part mainly because of two reasons:
1) recognizing arbitrary shaped text is still a challenging task, and 2)
prevalent non-trainable pipeline strategies between text detection and text
recognition will lead to suboptimal performances. To handle this
incompatibility problem, in this paper we propose an end-to-end trainable text
spotting approach named Text Perceptron. Concretely, Text Perceptron first
employs an efficient segmentation-based text detector that learns the latent
text reading order and boundary information. Then a novel Shape Transform
Module (abbr. STM) is designed to transform the detected feature regions into
regular morphologies without extra parameters. It unites text detection and the
following recognition part into a whole framework, and helps the whole network
achieve global optimization. Experiments show that our method achieves
competitive performance on two standard text benchmarks, i.e., ICDAR 2013 and
ICDAR 2015, and also obviously outperforms existing methods on irregular text
benchmarks SCUT-CTW1500 and Total-Text.

本文提出了一种名为 Text Perceptron 的端到端可训练文本检测方法，利用基于分割的高效检测器和新颖的形状变换模块，实现了文本检测和识别部分的全局优化，取得了在多个基准测试数据集上的优异性能表现。