Strokes Of Chinese Characters Research Articles

Fine Segmentation of Chinese Character Strokes Based on Coordinate Awareness and Enhanced BiFPN.

Considering the complex structure of Chinese characters, particularly the connections and intersections between strokes, there are challenges in low accuracy of Chinese character stroke extraction and recognition, as well as unclear segmentation. This study builds upon the YOLOv8n-seg model to propose the YOLOv8n-seg-CAA-BiFPN Chinese character stroke fine segmentation model. The proposed Coordinate-Aware Attention mechanism (CAA) divides the backbone network input feature map into four parts, applying different weights for horizontal, vertical, and channel attention to compute and fuse key information, thus capturing the contextual regularity of closely arranged stroke positions. The network's neck integrates an enhanced weighted bi-directional feature pyramid network (BiFPN), enhancing the fusion effect for features of strokes of various sizes. The Shape-IoU loss function is adopted in place of the traditional CIoU loss function, focusing on the shape and scale of stroke bounding boxes to optimize the bounding box regression process. Finally, the Grad-CAM++ technique is used to generate heatmaps of segmentation predictions, facilitating the visualization of effective features and a deeper understanding of the model's focus areas. Trained and tested on the public Chinese character stroke datasets CCSE-Kai and CCSE-HW, the model achieves an average accuracy of 84.71%, an average recall rate of 83.65%, and a mean average precision of 80.11%. Compared to the original YOLOv8n-seg and existing mainstream segmentation models like SegFormer, BiSeNetV2, and Mask R-CNN, the average accuracy improved by 3.50%, 4.35%, 10.56%, and 22.05%, respectively; the average recall rates improved by 4.42%, 9.32%, 15.64%, and 24.92%, respectively; and the mean average precision improved by 3.11%, 4.15%, 8.02%, and 19.33%, respectively. The results demonstrate that the YOLOv8n-seg-CAA-BiFPN network can accurately achieve Chinese character stroke segmentation.

Finger temperature-driven thermogalvainc gel-based smart pen: Utilized for identity recognition, stroke analysis, and grip posture assessment

Inappropriate pen-holding postures can result in increased muscle strain and reduced writing speed, and may even contribute to the onset of conditions such as myopia, arthritis, and cervical spondylosis. However, challenges persist in current one-size-fits-all pens toward actively monitoring handwriting habits due to structural complexity and indispensability of power supply. In this study, a finger temperature-driven thermoelectric gel-based smart pen has been invented for identity recognition, stroke analysis, and grip posture assessment by coupling thermogalvanic and piezoresistive effects derived from finger temperature and pressure on a PVA/gelatin gel. This dual-network gel exhibits not only a high thermopower of − 2.05 mV K−1 and a rapid response of 130 ms, but also full flexibility, significant resistance to desiccation, and a high tensile capacity of up to 300%. By integrating thermogalvanic gel units within a pen shaft, our self-powered smart pen achieved a recognition accuracy of 98.1% for eight basic Chinese character strokes with the aid of deep learning. Furthermore, with a four-channel expansion, the recognition accuracy reaches 98.8% for eight distinct identities and 99.1% for nine different pen-holding postures, respectively, while offering real-time visualization of monitoring results on a user-friendly human-machine interface. This machine learning-assisted self-powered smart gel pen presents a new viable solution for wearable bioelectronics toward future intelligent era.

Simple Conditional Spatial Query Mask Deformable Detection Transformer: A Detection Approach for Multi-Style Strokes of Chinese Characters

In the Chinese character writing task performed by robotic arms, the stroke category and position information should be extracted through object detection. Detection algorithms based on predefined anchor frames have difficulty resolving the differences among the many different styles of Chinese character strokes. Deformable detection transformer (deformable DETR) algorithms without predefined anchor frames result in some invalid sampling points with no contribution to the feature update of the current reference point due to the random sampling of sampling points in the deformable attention module. These processes cause a reduction in the speed of the vector learning stroke features in the detection head. In view of this problem, a new detection method for multi-style strokes of Chinese characters, called the simple conditional spatial query mask deformable DETR (SCSQ-MDD), is proposed in this paper. Firstly, a mask prediction layer is jointly determined using the shallow feature map of the Chinese character image and the query vector of the transformer encoder, which is used to filter the points with actual contributions and resample the points without contributions to address the randomness of the correlation calculation among the reference points. Secondly, by separating the content query and spatial query of the transformer decoder, the dependence of the prediction task on the content embedding is relaxed. Finally, the detection model without predefined anchor frames based on the SCSQ-MDD is constructed. Experiments are conducted using a multi-style Chinese character stroke dataset to evaluate the performance of the SCSQ-MDD. The mean average precision (mAP) value is improved by 3.8% and the mean average recall (mAR) value is improved by 1.1% compared with the deformable DETR in the testing stage, illustrating the effectiveness of the proposed method.

A Study on Influencing Factors of Dynamic Chinese Character Stroke Writing Behavior in CFL Beginners Using Digital Ink Technology

Abstract Exploring the intricacies of learning Chinese, this study leverages digital ink technology to observe the stroke writing behaviors of novice learners. We delved into the nuances of stroke order, direction, and the fluidity of writing, scrutinizing 2,131 Chinese characters to uncover the relationship between character structure and writing behavior. Our findings illuminate beginners’ challenges, offering a novel perspective on teaching Chinese characters that caters to the dynamic nature of writing and learning.

Research and design of Chinese cookie model with chun character based on OpenSCAD

In todays society, the 3D modelling field has become an indispensable part of peoples lives. Whether it is the metaverse, games, industry, or medicine, 3D modelling cannot be separated from them. The models used in various fields are also diverse. In the models of various fields, there are not many models for reference in the food fields biscuit modelling and there are no special views. This article creates a Chinese New Year biscuit with the Chinese handwritten character Spring on it based on the different styles and types of biscuit models in the food industry. OpenSCAD software was used to create this model. This article first introduces the methods and usage of OpenSCAD software that are encapsulated for modelling. Then this paper describes in detail how to layer and create biscuits layer by layer. How to use code to create a handwritten Chinese character and a cream biscuit completely in OpenSCAD is detailed by this article. For the result, this article implements a Chinese New Year biscuit with a reference to the Spring character written by the ancient Chinese calligrapher Yan Zhenqing. This work fully implements every stroke of handwritten Chinese characters in the cookie model, and these details have been recognized by the majority of people in the final evaluation.

An end-to-end chinese font generation network with stroke semantics and deformable attention skip-connection

The process of designing fonts traditionally requires a great deal of manpower, taking up to one year to complete one style set. In computer vision (CV) and computer graphics (CG), the automatic generation of Chinese fonts with a large number of complex glyphs remains a challenging and persistent problem. In this article, we propose an end-to-end network for generating 9169 Chinese characters without the need for human intervention. Due to the similarity of the strokes of the Chinese characters, different strokes will be recognized as the same type of stroke, resulting in errors. In this article maps the semantic information of Chinese character stroke categories to different stages of the encoder and adjusts the stroke category semantics in specific channels to ensure the synthesis of correct strokes. A deformation attention Skip-connection module was designed to adapt to font generation tasks by learning offsets in features from the decoder and encoder, weights in a cross-channel interactive manner, and adaptively re-scaling features using the learned weights and offsets. There are only a few parameters involved in this method, but it provides significant performance improvements. Compared to the prior art, the method employed by us outperforms it in terms of generating commercial fonts and handwritten fonts, which can help font designers with font design and improve the efficiency of font libraries.

Visual Attention Adversarial Networks for Chinese Font Translation

Currently, many Chinese font translation models adopt the method of dividing character components to improve the quality of generated font images. However, character components require a large amount of manual annotation to decompose characters and determine the composition of each character as input for training. In this paper, we establish a Chinese font translation model based on generative adversarial network without decomposition. First, we improve the method of image enhancement for Chinese character images. It helps the model learning structure information of Chinese character strokes to generate font images with complete and accurate strokes. Second, we propose a visual attention adversarial network. By using visual attention block, the network catches global and local features for constructing details of characters. Experiments demonstrate our method generates high-quality Chinese character images with great style diversity including calligraphy characters.

Mining and Utilizing Knowledge Correlation and Learners’ Similarity Can Greatly Improve Learning Efficiency and Effect: A Case Study on Chinese Writing Stroke Correction

Using AI technology to improve teaching and learning is an important goal of educational sustainability. By mining the correlation between knowledge points, the discrete knowledge points can be integrated to improve the knowledge density and reduce the learning task. In addition, the successful experiences of similar learners can be shared, thus shortening the learning path of new learners. To change the common situation of irregular writing stroke order, to teach and correct stroke order effectively, this study uses association rules to explore the potential correlation between error-prone Chinese characters based on a large number of learners’ writing records, and then summarizes and sorts out a set of error-prone Chinese characters based on this. Every Chinese character contained in an error-prone category has a common error-prone feature. By correcting this error, it can be extended to every Chinese character of this category, and the learning efficiency of Chinese character strokes can be improved tens of times. In the training and testing system with a Chinese character error-prone character set, combined with the improved collaborative filtering algorithm, a learner-based personalized error-prone Chinese character recommendation model was proposed. Experimental results showed that the Apriori algorithm with lift measure can excavate effective strong association rules and provide an important reference for the character set table. The improved collaborative filtering algorithm can make use of the similarity between learners, share successful learning experiences, provide a personalized recommendation service for error-prone Chinese characters, and the recommendation performance is higher than that of the traditional collaborative filtering model. In the test of different types of learning groups, there are obvious differences between the independent pre-test and the post-training test, which effectively corrects the irregular writing habits, and further indicates that the excavation of knowledge correlation and the combination of learners’ similarity can effectively improve the efficiency and effect of teaching and learning.

浅析印尼小学汉字线上教学 ——以光明学校与信望爱学校为例 A BRIEF ANALYSIS OF INDONESIA PRIMARY SCHOOLS CHINESE CHARACTERS ONLINE TEACHING (Study Cases at Guang Ming and Ipeka School)

摘要在汉语的学习过程中，汉字无疑成为学生的难题之一，同时也成为汉语教师在教学中的挑战之一。印尼至2020年3月中旬全国开始线上课，学校如何进行线上汉字课便是本研究的研究重点。本研究主要通过课堂观察与教师访谈对光明学校与信望爱学校进行研究。目的是为了更深入了解光明学校与信望爱学校疫情期间的线上汉字教学的情况。通过研究得知，无论在识字教学还是写字教学，教师大部分都使用PPT教学生识字和写汉字的笔顺笔画。教师会把汉字的字形跟图片或者GIF汉字笔顺放在PPT上展示给学生看。教师也会使用一些教学网站给学生设计识字练习。在汉字练习方面，教师最常使用的汉字练习方式是让学生在练习册上描汉字或者写汉字。 关键词：线上汉字教学；印尼；小学 AbstractIn the process of learning Chinese, Chinese characters undoubtedly become one of the difficult problems for students, and also one of the challenges for Chinese teachers in teaching. This study focused on Guang Ming School and IPEKA School through classroom observation and teacher interviews. The aim was to gain a deeper understanding of the online Chinese character pedagogy during the outbreak in the two schools. Through research, it is learned that in both literacy teaching and writing teaching, teachers mostly use PPT to teach students to read and write the strokes of Chinese characters. Teachers will put the glyphs and pictures or GIF Strokes with Chinese characters on the PPT and show them to the students. Teachers also use some teaching websites to design literacy exercises for students. In terms of Chinese character practice, the most commonly used Chinese character practice method by teachers is for students to draw or write Chinese characters on the exercise books.Keywords: Online Chinese characters teaching; Indonesia; Primary School

The Instructional Design of Chinese Characters’ Stroke Order Motion Graphics Based on Cognitive Load Theory

This research aims to develop stroke order motion graphics for Chinese characters to solve the problem of memorising Chinese characters’ stroke order in the learning process. This research adopted cognitive load theory and the ADDIE model as an instructional design process guide. Herbart’s four-stage teaching method is used as a guide for the motion graphics presentation module. Based on the characteristics of Malaysian students who learn Chinese as their second language, motion graphics for Chinese characters’ stroke order learning were developed. The expert evaluation was conducted to identify problems, and modifications were done to improve the created prototype. A total of six Chinese characters’ stroke order motion graphics have been successfully developed. The result shows that cognitive load theory provides an effective solution for developing Chinese characters’ stroke order motion graphics. The ADDIE model also offered a significant direction for the instructional design process. In addition, to be more effective in Chinese character stroke order teaching, interface design must consider the relevant teaching effects of cognitive load theory. However, making the prototype in advance can avoid large-scale modifications in the later process. The successful development of the Chinese characters’ stroke order motion graphics allows teaching Chinese character stroke order in Malaysia to be carried out more effectively.

Comparing the Effects of Stroke-Appearing and Stroke-Disappearing on Learning the Order of Strokes in Chinese Characters

Different approaches to stimulating perceptions in learning can be easily designed with technology-enhanced learning systems. This study aimed to explore how different approaches can influence learners' perceptions that may negatively or positively affect their learning performance of writing Chinese characters using the correct Chinese order of strokes (COS). We therefore designed an e-learning system which was subdivided into two modes: stroke-appearing (i.e., using red to mark incorrect strokes) and stroke-disappearing (i.e., using blanks to mark incorrect strokes) to indicate strokes written in the incorrect order. We then investigated the modes that would facilitate a higher level of attention and better learning outcomes. A total of 10 third-grade elementary school students participated in the experiment, divided into two test groups. Their EEG data were collected, and time series analysis and t-tests were utilized to analyze the differences. The results indicated that: (1) there was a significant difference in the attention levels of the students practicing with the stroke-appearing and stroke-disappearing modes when learning COS, and (2) there was a significant difference in the learning outcomes of the students practicing with the stroke-appearing and stroke-disappearing modes when learning COS. These findings support the specific role of stroke order knowledge in learning Chinese characters and the need for the design of an effective method for teaching children to learn Chinese characters.

FSPRM: A Feature Subsequence Based Probability Representation Model for Chinese Word Embedding

Chinese word embedding models capture Chinese semantics based on the character feature of Chinese words and the internal features of Chinese characters such as radical, component, stroke, structure and pinyin. However, some features are overlapping and most methods do not consider their relevance. Meanwhile, they express words as point vectors that cannot better capture different aspect semantics of Chinese words. In this paper, we propose a Feature Subsequence based Probability Representation Model (FSPRM) for learning Chinese word embeddings, in which we first integrate the morphological and phonetic features (stroke, structure and pinyin) of Chinese characters and learn their relevance by designing a feature subsequence to capture relatively comprehensive semantics of Chinese words, then feature probability distribution is proposed for capturing different aspect meanings of Chinese words based on the three internal features and probability representation by estimating its mean as the sum of feature subsequences. Chinese words with similar features may have similar semantics, then we map Chinese words to feature probability distributions and design a similarity-based objective for predicting the contextual words of the target word to learn their semantics. Extensive experiments on word analogy, word similarity, text classification and named entity recognition tasks demonstrate that the proposed method outperforms most state-of-the-art approaches.

An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing.

A Semantic Analysis and Community Detection-Based Artificial Intelligence Model for Core Herb Discovery from the Literature: Taking Chronic Glomerulonephritis Treatment as a Case Study

The Traditional Chinese Medicine (TCM) formula is the main treatment method of TCM. A formula often contains multiple herbs where core herbs play a critical therapeutic effect for treating diseases. It is of great significance to find out the core herbs in formulae for providing evidences and references for the clinical application of Chinese herbs and formulae. In this paper, we propose a core herb discovery model CHDSC based on semantic analysis and community detection to discover the core herbs for treating a certain disease from large-scale literature, which includes three stages: corpus construction, herb network establishment, and core herb discovery. In CHDSC, two artificial intelligence modules are used, where the Chinese word embedding algorithm ESSP2VEC is designed to analyse the semantics of herbs in Chinese literature based on the stroke, structure, and pinyin features of Chinese characters, and the label propagation-based algorithm LILPA is adopted to detect herb communities and core herbs in the herbal semantic network constructed from large-scale literature. To validate the proposed model, we choose chronic glomerulonephritis (CGN) as an example, search 1126 articles about how to treat CGN in TCM from the China National Knowledge Infrastructure (CNKI), and apply CHDSC to analyse the collected literature. Experimental results reveal that CHDSC discovers three major herb communities and eighteen core herbs for treating different CGN syndromes with high accuracy. The community size, degree, and closeness centrality distributions of the herb network are analysed to mine the laws of core herbs. As a result, we can observe that core herbs mainly exist in the communities with more than 25 herbs. The degree and closeness centrality of core herb nodes concentrate on the range of [15, 40] and [0.25, 0.45], respectively. Thus, semantic analysis and community detection are helpful for mining effective core herbs for treating a certain disease from large-scale literature.

'Write' but not 'spell' Chinese characters with a BCI-controlled robot.

Visual brain-computer interface (BCI) systems have made tremendous process in recent years. It has been demonstrated to perform well in spelling words. However, different from spelling English words in one-dimension sequences, Chinese characters are often written in a two-dimensional structure. Previous studies had never investigated how to use BCI to 'write' but not 'spell' Chinese characters. This study developed an innovative BCI-controlled robot for writing Chinese characters. The BCI system contained 108 commands displayed in a 9*12 array. A pixel-based writing method was proposed to map the starting point and ending point of each stroke of Chinese characters to the array. Connecting the starting and ending points for each stroke can make up any Chinese character. The large command set was encoded by the hybrid P300 and SSVEP features efficiently, in which each output needed only 1s of EEG data. The task-related component analysis was used to decode the combined features. Five subjects participated in this study and achieved an average accuracy of 87.23% and a maximal accuracy of 100%. The corresponding information transfer rate was 56.85 bits/min and 71.10 bits/min, respectively. The BCI-controlled robotic arm could write a Chinese character '' with 16 strokes within 5.7 seconds for the best subject. The demo video can be found at https://www.youtube.com/watch?v=A1w-e2dBGl0. The study results demonstrated that the proposed BCI-controlled robot is efficient for writing ideogram (e.g. Chinese characters) and phonogram (e.g. English letter), leading to broad prospects for real-world applications of BCIs.

Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot

As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly.

The role of features in Chinese character recognition

Chinese characters contain three levels of visual information: strokes, radicals, and structures. Stroke, as the basic unit of Chinese characters, is the foundation of the formation of Chinese characters. Some research has shown that strokes can enhance the semantic and orthographic representation in the process of Chinese character acquisition. This shows that the processing of Chinese characters is based on feature processing. This is consistent with the feature processing theory of letter and object recognition. Recent research on visual recognition shows that the recognition of letters and words is based on the termination and the other features (vertices and midsegment) used in object recognition. In this study, we used delayed segment technique on Chinese character recognition to examine the importance of terminations, vertices and midsegment in different masking degree. Experiment 1 selected about 43 high frequency Chinese characters with left-right structures and asked the participants to naming Chinese characters. In experiment 1, we try to examine the importance of terminations, vertices and midsegments based on line unit and consider the degree of masking these features. It was found that the main effect of masking degree was significant. The masking of 35% and 55% of the features makes the RTs significantly longer. The main effect of feature type was significant. Masking vertices and midsegment can make RTs significantly longer. But termination didn’t show its feature effect in experiment 1. When masking these three features (vertices, midsegment and termination) in experiment 1, the features are based on line units, which may destroy the integrity of strokes in Chinese characters. That termination effects couldn’t be obvious in experiment 1 may be caused by this reason. To verify this, in experiment 2, we explore the vertices, midsegment and termination based on stroke units. The results show that the main effect of masking degree was significant, and the RTs were longer by masking 35% and 55% degree. The main effect of feature types was significant. RTs on character recognition are significantly longer by masking the vertices, midsegment and termination. This shows that the vertices, midsegment and termination based on stroke unit are also important in Chinese character recognition. Based on the above research results, it can be seen that the key features on Chinese character recognition are in line with the neuronal recycling hypothesis. According to neuronal recycling hypothesis, human’s ability to learn from culture depends on the retrieval of preexisting brain circuits, and the key features in word recognition may be derived from our key features in object processing. The hook is considered to be a special termination in Chinese characters. The results of experiment 2 also emphasized the key role of hook features in character recognition.

Mixed Vertical-and-Horizontal-Text Traffic Sign Detection and Recognition for Street-Level Scene

Much effort has been dedicated to text-based traffic sign detection and recognition. However, there are still two problems. First, unlike English traffic signs with only horizontal text, Chinese traffic signs have both horizontal and vertical text. To the best of our knowledge, there is nothing in the literature about simultaneous recognition of both horizontal and vertical text in Chinese text-based traffic signs. Second, most existing methods focus on wild and expressway scenes; few focus on street scenes. To solve these problems, we propose a mixed vertical-and-horizontal-text traffic sign detection and recognition algorithm for street-level scene. First, an effective combination of different red, green and blue components is used to distinguish the traffic signs from many objects of similar color in the very complex street scenes. Second, unlike English letters, the strokes of many Chinese characters are unconnected, which may result in that a character will be detected as two or more characters. Unlike the English text lines, which are only horizontal, the Chinese text lines on text-based traffic signs are usually both in horizontal and vertical directions. Our proposed method uses the position and structural information of the characters to form the text lines. A dataset of Chinese text-based traffic signs is collected. Experimental results indicate the effectiveness of the proposed method.

GANCCRobot: Generative adversarial nets based chinese calligraphy robot

Robotic calligraphy, as a typical application of robot movement planning, is of great significance for the inheritance and education of calligraphy culture. The existing implementations of such robots often suffer from its limited ability for font generation and evaluation, leading to poor writing style diversity and writing quality. This paper proposes a calligraphic robotic framework based on the generative adversarial nets (GAN) to address such limitation. The robot implemented using such framework is able to learn to write fundamental Chinese character strokes with rich diversities and good quality that is close to the human level, without the requirement of specifically designed evaluation functions thanks to the employment of the revised GAN. In particular, the type information of the stroke is introduced as condition information, and the latent codes are applied to maximize the style quality of the generated strokes. Experimental results demonstrate that the proposed model enables a calligraphic robot to successfully write fundamental Chinese strokes based on a given type and style, with overall good quality. Although the proposed model was evaluated in this report using calligraphy writing, the underpinning research is readily applicable to many other applications, such as robotic graffiti and character style conversion.

Design and development of an EOG-based simplified Chinese eye-writing system

Eye movement information provides an alternative way for patients with muscular and neurological disorders to communicate with other people or devices. Electrooculography (EOG) is an effective eye movement recording method that has been widely applied to design human–computer interaction systems. In this paper, we propose a simplified Chinese eye-writing system based on EOG (EsCew) by identifying the basic strokes of Chinese characters. Specifically, we first use a bandpass digital filter to preprocess the raw EOG signals to suppress noise interference. Then, we determine the effective eye movement segments according to different strokes by detecting blink signals using the sliding window technique. On this basis, we establish basic stroke templates in terms of the handwriting characteristics of Chinese characters. To reduce the computational complexity, the DTW algorithm is adopted to classify the EOG segments. Finally, we match the stroke sequence with the encoded Chinese characters to obtain the final recognition results. In the lab environment, the recognition experiments are performed on 10 most representative Chinese characters, i.e., ▪. The average accuracies of the basic strokes and Chinese characters are 93.998% and 94.52%, respectively. The experimental results validate the feasibility of the proposed EsCew system. [Display omitted]