With the significant power of deep learning architectures, researchers have made much progress on effectiveness and efficiency of text detection in the past few years. However, due to the lack of consideration of unique characteristics of text components, directly applying deep learning models to perform text detection task is prone to result in low accuracy, especially producing false positive detection results. To ease this problem, we propose a lightweight and context-aware deep convolutional neural network (CNN) named as CE-Text, which appropriately encodes multi-level channel attention information to construct discriminative feature map for accurate and efficient text detection. To fit with low computation resource of embedded systems, we further transform CE-Text into a lighter version with a frequency based deep CNN compression method, which expands applicable scenarios of CE-Text into variant embedded systems. Experiments on several popular datasets show that CE-Text not only has achieved accurate text detection results in scene images, but also could run with fast performance in embedded systems.

借助深度学习架构的强大功能，研究人员在过去几年中在文本检测的有效性和效率方面取得了很大进展。然而，由于缺乏对文本组件独特特性的考虑，直接应用深度学习模型进行文本检测任务容易导致准确率低，尤其是产生误报检测结果。为了缓解这个问题，我们提出了一种名为 CE-Text 的轻量级和上下文感知的深度卷积神经网络 (CNN)，它适当地编码了多级通道注意信息，以构建有区别的特征图，以实现准确和高效的文本检测。为了适应嵌入式系统的低计算资源，我们进一步将 CE-Text 转换为更轻的版本，使用基于频率的深度 CNN 压缩方法，它将 CE-Text 的适用场景扩展到各种嵌入式系统。对几个流行数据集的实验表明，CE-Text 不仅在场景图像中取得了准确的文本检测结果，而且在嵌入式系统中运行速度也很快。