# An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition
## 2 Related Works  
Research on IAHCCR has been underway for several years, and a complete recognition method usually consists of three stages: preprocessing, feature extraction and classification. The work related to different stages will be described in detail below.  
In the preprocessing stage, as mentioned in Section 1, IAHCCR can use the method of OLHCCR. Character normalization can reduce within-class variation and improve recognition accuracy. Traditional methods require the normalization of characters to a uniform size. Linear normalization, which causes character-shape changes, has been superseded by other methods [11]. For example, nonlinear normalization [9], pseudo-2D normalization and line-density projection interpolation [11]. End-to-end methods do not require normalization to a fixed-size vector but, rather, normalize the distribution of coordinate sequences [24].  
Feature extraction and classification are two separate stages in the traditional IAHCCR method. In the stage of manual feature extraction, compared with the character image drawn using the original coordinates, the method of decomposing local strokes into different directions to form multiple feature maps achieves higher recognition accuracy, such as eight-directional feature maps [10]. Especially for IAHCCR, the whole Chinese character is more like a curve function defined on a two-dimensional plane that can be expanded by [PERSON]. [PERSON] et al. [25] proposed higher-order directional features. Representing Chinese characters as directional features has been the standard approach for a long time. Classifiers are also very important for IAHCCR. To further improve the recognition rate and recognition speed, the learning vector quantization technique-based [26] multi-level classification technique is reported for IAHCCR in [27]. [PERSON] et al. introduced locality-sensitive sparse representation-based classifiers (LSRC) [28] into IAHCCR, and achieved a higher recognition rate than MQDF [25]. In order to further improve the recognition accuracy of LSRC, a loss function is designed to minimize the reconstruction error of each training pattern and make the reconstruction of each training pattern as close as possible to the optimized prototype of its class is suggested, which significantly improves recognition accuracy [29].  
As deep-learning techniques have made great achievements in other fields, the deep neural network was introduced into IAHCCR. In the deep-learning-based IAHCCR algorithm, feature extraction and classification are integrated. For IAHCCR, [PERSON] et al. [23] proposed a nine-layer convolutional-neural-network model combined with data-augmentation technology which significantly improved the recognition rate. Like other image recognition tasks [30], the performance of this method requires a large amount of data to ensure the performance, so data-augmentation technology is needed to expand the data, although it also requires more memory consumption and a higher number of parameters. [PERSON] et al. [20] proposed an end-to-end recognizer based on recurrent neural networks. This method directly recognizes character sequences without converting characters into feature vectors. In order to further improve the recognition accuracy, [PERSON]. [4] proposed an RNN system with two new computing architectures added. Table 1 summarizes related works. In this paper, we combine the advantages of CNN and RNN to propose an end-to-end CNN model for directly recognizing sequences.