# Recognition of Eye-Written Characters Using Deep Neural Network
## 1 Introduction  
Keyboards, mice, and touchscreens represent the most popular input devices for human-computer interaction (HCI) in recent decades, and they are useful for general everyday purposes. Additionally, novel types of interfaces for computer systems have been developed for specialized applications, such as education, medical care, arts, controlling robots, and games utilizing gestures [1], voices [2], pens [3], and other devices [4, 5]. Biosignal processing is drawing attention to these novel interfaces because it enables direct interactions between body movements and a computer. Directly interacting with computers through biosignals could significantly improve user experience. Biosignals used for HCI include electroencephalograms (EEG), electromyograms (EMG), and electrooculograms (EOG) [6, 7, 8]. EOG are directly related to eye movements and can thus be used for eye-tracking. Because of the difference in electric potentials between the retina and cornea of the eye, the potential increases where the cornea approaches as the eye moves [9]. Eye movements can be measured using optical or infrared cameras [10, 11, 12, 13]. Camera-based methods have higher accuracy than EOG methods but suffer from limitations such as their high cost, complicated setup, and inconsistent recognition rates because of the variability in eyelid/eyelash movements among different individuals and contrast differences depending on the surrounding environment [4]. EOG-based eye tracking is relatively cheap and is not affected by lighting or the physical condition of the eyelids. However, it is difficult to obtain clean data with this method because various signals from the body are measured together with EOG, and these are difficult to separate. Previous studies indicate that EOG-based eye-tracking often attempts to estimate eye movements during a very short period (less than 1 s) with simple and directional movements [14; 15; 16; 17; 18]. [PERSON] et al. recognized a maximum of 24 patterns with this approach [19]. They classified eye movement in 24 directions with an average accuracy of 87.1%, but the performance was unstable, and the eyes needed to be turned up to 75\({}^{\circ}\), which is unnatural and inconvenient. Recently, the concept of eye writing was introduced to overcome the limitations of conventional EOG-based methods. Eye writing involves moving the eyes such that the gaze traces the form of a letter, which increases the amount of information transfer compared to conventional EOG-based eye tracking. The degree of freedom is as high as the number of letters we may write. Recent studies have shown that eye writing can be used to trace Arabic numerals, English alphabets, and Japanese katakana characters [20; 21]. To recognize eye-written characters, various pattern-recognition algorithms have been proposed. [PERSON] et al. proposed a system for recognizing eye-written Arabic numerals and four arithmetic symbols [22]. The system was developed using a heuristic algorithm and achieved a 75.5% accuracy (believability). [PERSON] et al. utilized a hidden Markov model to recognize 12 patterns of Japanese katakana and achieved an 86.5% accuracy in writer-independent validation [20]. [PERSON] et al. utilized dynamic time warping (DTW) to achieve an 87.38% accuracy for 36 patterns of numbers and English alphabets [21]. [PERSON] et al. showed that the accuracy could be increased by combining dynamic positional warping, a modification of DTW, with a support vector machine (SVM) [23]. They achieved a 95.74% accuracy for Arabic numbers, which was 3.33% points higher than when only DTW was used for the same dataset. Increasing the recognition accuracy is critical for eye writing when it is used as an HCI tool. This paper proposes a method to increase the accuracy of the conventional method using a deep neural network. The proposed method minimizes the preprocessing procedures and automatically finds the features in convolutional network layers.