深度卷积神经网络（CNN）在模式识别（例如识别图像中的文本）方面获得了巨大的成功。但是现有的基于CNN的框架仍然有几个缺点：1）传统的池化操作可能会丢失重要的特征信息并且是无法学习的；2）传统的卷积运算优化缓慢，并且不同层次的层次特征没有得到充分利用。..

Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

The Deep Convolutional Neural Networks (CNNs) have obtained a great success for pattern recognition, such as recognizing the texts in images. But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.In this work, we address these problems by developing a novel deep network model called Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet). Specifically, we design a further dense block called intensive block to extract the feature information, where the original inputs and two dense blocks are connected tightly. To encode data appropriately, we present the concepts of dense fusion block and further dense fusion opera-tions for our new intensive block. By adding short connections to different layers, the feature flow and coupling between layers are enhanced. We also replace the traditional convolution by depthwise separable convolution to make the operation efficient. To prevent important feature information being lost to a certain extent, we use a convolution operation with stride 2 to replace the original pooling operation in the customary transition layers. The recognition results on large-scale Chinese string and MNIST datasets show that our IntensiveNet can deliver enhanced recog-nition results, compared with other related deep models.