Recent progress has been made on developing a unified framework for joint
text detection and recognition in natural images, but existing joint models
were mostly built on two-stage framework by involving ROI pooling, which can
degrade the performance on recognition task. In this work, we propose
convolutional character networks, referred as CharNet, which is an one-stage
model that can process two tasks simultaneously in one pass. CharNet directly
outputs bounding boxes of words and characters, with corresponding character
labels. We utilize character as basic element, allowing us to overcome the main
difficulty of existing approaches that attempted to optimize text detection
jointly with a RNN-based recognition branch. In addition, we develop an
iterative character detection approach able to transform the ability of
character detection learned from synthetic data to real-world images. These
technical improvements result in a simple, compact, yet powerful one-stage
model that works reliably on multi-orientation and curved text. We evaluate
CharNet on three standard benchmarks, where it consistently outperforms the
state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements
of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on
Total-Text, on end-to-end text recognition. Code is available at:
this https URL

本文提出了 CharNet 模型，是一种单阶段模型，能够同时处理文本检测和识别，输出包围框和对应字符标签，并采用迭代式字符检测方法，在三个标准基准测试中表现出对现有方法显著的性能改进。