A convolutional neural network (CNN) has been successfully applied to in-air handwritten-Chinese-character recognition (IAHCCR). However, the existing models based on CNN for IAHCCR need to convert the coordinate sequence of a character into images. This conversion process increases training and classifying time, and leads to the loss of information. In order to solve this problem, we propose an end-to-end classifier based on CNN for IAHCCR in this paper, which, to knowledge, is novel for online handwritten-Chinese-character recognition (OLHCCR). Specifically, our model based on CNN directly takes the original coordinate sequence of an in-air handwritten-Chinese-character as input, and the output of the full connection layer is pooled by global average pooling to form a fixed-size feature vector, which is sent to softmax for classification. Our model can not only directly process coordinate sequences such as the models based on recurrent neural network (RNN), but can also obtain the global structure information of characters. We conducted experiments on two datasets, IAHCC-UCAS2016 and SCUT-COUCH2009. The experimental results show a comparison with existing CNN models based on image processing or RNN-based methods; our method does not require data augmentation techniques nor an ensemble of multiple trained models, and only uses a more compact structure to obtain higher recognition accuracy.

In multi-label learning, each object is represented by a single instance and is associated with more than one class labels, where the labels might be correlated with each other. As we all know, exploiting label correlations can definitely improve the performance of a multi-label classification model. Existing methods mainly model label correlations in an indirect way, i.e., adding extra constraints on the coefficients or outputs of a model based on a pre-learned label correlation graph. Meanwhile, the high dimension of the feature space also poses great challenges to multi-label learning, such as high time and memory costs. To solve the above mentioned issues, in this paper, we propose a new approach for Multi-Label Learning by Correlation Embedding, namely MLLCE, where the feature space dimension reduction and the multi-label classification are integrated into a unified framework. Specifically, we project the original high-dimensional feature space to a low-dimensional latent space by a mapping matrix. To model label correlation, we learn an embedding matrix from the pre-defined label correlation graph by graph embedding. Then, we construct a multi-label classifier from the low-dimensional latent feature space to the label space, where the embedding matrix is utilized as the model coefficients. Finally, we extend the proposed method MLLCE to the nonlinear version, i.e., NL-MLLCE. The comparison experiment with the state-of-the-art approaches shows that the proposed method MLLCE has a competitive performance in multi-label learning.