Reading irregular scene text of arbitrary shape in natural images is still a
challenging problem, despite the progress made recently. Many existing
approaches incorporate sophisticated network structures to handle various
shapes, use extra annotations for stronger supervision, or employ hard-to-train
recurrent neural networks for sequence modeling. In this work, we propose a
simple yet strong approach for scene text recognition. With no need to convert
input images to sequence representations, we directly connect two-dimensional
CNN features to an attention-based sequence decoder which guided by holistic
representation. The holistic representation can guide the attention-based
decoder focus on more accurate area. As no recurrent module is adopted, our
model can be trained in parallel. It achieves 1.5x to 9.4x acceleration to
backward pass and 1.3x to 7.9x acceleration to forward pass, compared with the
RNN counterparts. The proposed model is trained with only word-level
annotations. With this simple design, our method achieves state-of-the-art or
competitive recognition performance on the evaluated regular and irregular
scene text benchmark datasets.

本研究提出一种基于全局特征引导注意力的二维卷积神经网络和序列解码器来识别场景文本的简单但强大方法，训练时仅使用单词级别的注释，相比循环神经网络，能够在前向和后向传递中获得 1.3x 至 9.4x 不等的加速。在评估的正常和不规则的场景文本基准数据集上实现了最先进或竞争性的识别性能。