# An End-to-End Classifier Based on CNN for In-Air Handwritten-Chinese-Character Recognition
## 1 Introduction  
In the field of image processing and pattern recognition, the recognition of online handwritten characters is important work and many impressive achievements have been achieved [1, 2, 3, 4, 5]. The data processed by online handwriting recognition is a series of coordinate sequences, which are usually used in human-computer interaction devices, such as handwriting tablets, smart phones and pads. The input range of the traditional human-computer interaction method is limited by the size of the touch device, and damage to the local area will make the entire device unusable. In this context, a new way of human-computer interaction, vision-based and in-handwriting, has attracted more and more researchers' interest [6, 7, 8], e.g., we can use in-air handwriting to switch TV channels remotely or adjust the temperature of an air conditioner. Compared with the traditional online handwriting a using touch pad or wearable device, visual-based in-air handwriting experiences fewer space constraints and allows the writers to freely write in the air; the generated character has no pen-lift information and is finished in one stroke. The jitter of the stroke of a character is quite serious and the strokes overlap each other. These characteristics of in-air handwriting characters bring more difficulties for IAHCCR. Some examples of in-air handwriting Chinese characters and traditional handwritten Chinese characters can be seen in Figure 1.  
As in-air handwriting is a new development of traditional online handwriting, the methods used for OLHCCR are available for IAHCCR. Traditional OLHCCR methods donot directly recognize the original coordinate sequence of an online handwritten Chinese character, but extract features from a preprocessed coordinate sequence according to specific classification rules and specific domain knowledge [9, 10]. Before deep learning was introduced into OLHCCR, statistical features, classification algorithms based on statistical features and preprocessing algorithms had always been the research hotspots of OLHCCR, and show excellent performance in OLHCCR [11, 12]. Related methods have been introduced into IAHCCR, e.g., linear or nonlinear normalization [9, 11], eight-directional features [10], modified quadratic discriminant functions (MQDF) [13], etc. In recent years, deep learning has achieved great success in the fields of computer vision and pattern recognition [14, 15, 16], and also been successfully applied to OLHCCR [17, 18, 19, 20]. Compared with the above-mentioned traditional classification models, the models based on deep learning show an overwhelming advantage in OLHCCR. However, to our knowledge, all the existing models based on CNN for OLHCCR do not directly recognize the coordinate sequence of an online handwritten Chinese character, but convert the coordinate sequence into images or vectors [2, 23, 21, 22, 23]. As shown in Figure 2, this conversion process not only causes the loss of training and recognition time, but also only utilizes the spatial information of characters and loses the temporal information of the coordinate sequence, so it is difficult to obtain a higher recognition rate. Since the CNN based model uses a large-scale convolution kernel, it often requires a large number of training patterns and leads to more memory consumption. Unlike the CNN models, the models based on RNN can directly process the coordinate sequences of online handwritten Chinese characters and outperform most CNN structures [4, 24]. Although RNN-based models are suitable for processing sequence data, they are less suitable for processing long sequence data than CNN and ignore the global structures of online handwritten Chinese characters. In-air handwritten Chinese characters usually contain hundreds of points, so RNN-based models will consume a lot of computation time. Based on the above analysis, we propose an end-to-end classifier based on CNN for IAHCCR in this paper which has the advantages of both CNN and RNN.