Character Strokes Research Articles

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.

FontRL: Chinese Font Synthesis via Deep Reinforcement Learning

Automatic generation of Chinese fonts is a valuable but challenging task in areas of AI and Computer Graphics, mainly due to the huge amount of Chinese characters and their complex glyph structures. In this paper, we propose FontRL, a novel method for Chinese font synthesis by using deep reinforcement learning. Specifically, we first train a deep reinforcement learning model to obtain the Thin-Plate Spline (TPS) transformation that is able to modify the reference stroke skeleton in a mean font style into the skeleton of a required style for each stroke of every unseen Chinese character. Afterwards, we utilize a CNN model to predict the location and scale information of these strokes, and then assemble them to get the skeleton of the corresponding character. Finally, we convert each synthesized character skeleton into the glyph image via an image-to-image translation model. Both quantitative and qualitative experimental results demonstrate the superiority of the proposed FontRL compared to the state of the art. Our code is available at https://github.com/lsflyt-pku/FontRL.

In Codice Ratio: A crowd-enabled solution for low resource machine transcription of the Vatican Registers

In Codice Ratio is a research project to study techniques for analyzing the contents of historical documents conserved in the Vatican Apostolic Archives. In this paper, we present our efforts to develop a system to support the automatic transcription of medieval manuscripts, while maintaining the training data collection effort minimal. We focus on crowdsourcing as a means for scalable, expertless training data collection: using crowdsourced character symbols, we train a custom convolutional neural network able to jointly learn correct character shape identification and character recognition. Our approach generates candidate transcriptions by submitting over-segmented character strokes and their combinations to this classifier, while ranking and choosing the most promising outputs by combining the recognition confidence with character and word level statistical language models.We conducted experiments on an unreleased corpus, the Vatican Registers: training our system on 20 pages annotated by the crowd, we were able to obtain good results (19% CER); comparisons to an off-the-shelf system trained with 20 pages annotated with the same process, and to a professional system trained with more than 300 pages transcribed by skilled paleographers demonstrate the opportunities of the proposed approach.

Tamil Character Recognition in Palm Leaf Manuscripts

Tamil characters are of historical significance. The shapes of the character and its writing continued to be changed by several reformers in each century until the period of the nineteenth century. The Tamil characters in the palm leaf manuscripts are based on the author's writing style and strokes in use at the time. Reading palm leaf manuscripts is a challenge for the modern generation who are unaware of the character strokes and writing patterns written in earlier times, and the younger generation neglects to read and understand the contents written in palm leaf manuscripts. To read Tamil palm leaf manuscripts it is necessary to remember the shapes of the character that has changed over time and to compare and recognize the characters. This research paper explains how to recognize the Tamil characters written in palm leaf manuscripts by computer. By this research, the Tamil characters can be compared with different strokes and shapes and the exact character can be recognized accurately and quickly.

The effect of character stroke width on legibility: The relationship between duty ratio and contrast threshold

Stroke width is an important factor affecting letter legibility. Although there seems to be a critical or optimal value for stroke width, it has been difficult to estimate quantitatively because shape deterioration and the increase of stroke width are covariates. This study attempted to predict the optimal value of stroke width considering it as a duty ratio. The duty ratio is related to the amplitude of the fundamental frequency, with the maximum amplitude of the fundamental frequency occurring at a duty ratio of 0.5. The fundamental frequency decreases when the duty ratio is set either lower or higher than 0.5. Based on previous research indicating that letters also have a critical band (CB), or the frequency band which contributes most to letter recognition, which corresponds to their fundamental frequency, we hypothesized that the amplitude of CB is related to the legibility of letters. We measured the contrast thresholds using square-wave gratings and letters of differing duty ratios. In both the grating and letter stimuli, the amplitude of CB reached its maximum when the duty ratio was around 0.5. Contrast thresholds for stimuli with various duty ratios were accurately predicted by the amplitudes of CB and the contrast thresholds in the individual’s sine-wave grating. Our results suggest that the amplitude of a single critical component contributes significantly to the legibility of letters, even though they contain diverse frequency components.

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]

Nighttime visual recognition performance of light emitting diode traffic signs

Light emitting diode (LED) traffic signs have been deployed on many urban roadways recently. However, a lack of uniform engineering criteria for the luminance level of LED traffic signs in China brought up consequences such as inaccurate recognition of signs for drivers as well as potential traffic accidents. This paper explores the possible luminance range of LED traffic signs through full-scale field static experiments. Visual recognition distance, character height, character stroke, sign background color, sign dimension, and sign complexity degree are taken into account. The visual recognition luminance value required for drivers are recorded. Experiment results reveal that: for character symbol, the background color of the board and character strokes have no significant effect on driver’s visual recognition performance; for graphic symbol, the higher of graphic complex degree, the higher luminance value required for driver to recognize. This study summarizes the luminance thresholds of different kinds of LED signs for different roadway classes. The findings of this study would provide reference for future design of LED traffic signs.

Matching Seqlets: An Unsupervised Approach for Locality Preserving Sequence Matching.

In this paper, we propose a novel unsupervised approach for sequence matching by explicitly accounting for the locality properties in the sequences. In contrast to conventional approaches that rely on frame-to-frame matching, we conduct matching using sequencelet or seqlet, a sub-sequence wherein the frames share strong similarities and are thus grouped together. The optimal seqlets and matching between them are learned jointly, without any supervision from users. The learned seqlets preserve the locality information at the scale of interest and resolve the ambiguities during matching, which are omitted by frame-based matching methods. We show that our proposed approach outperforms the state-of-the-art ones on datasets of different domains including human actions, facial expressions, speech, and character strokes.

Error Analysis on Han Character Stroke Writing (汉字 Hànzì) of Mandarin Language Teachers (Participants on Training for Senior High Schools/Vocational Schools/ Islamic High Schools Chinese Language Teachers on Critical Literacy and HOTS)

ABSTRACT
 Mandarin language has been one of the subjects in 2013 Curriculum as one of the foreign languages taught in schools starting from elementary to universities. Due to high interest in Chinese language in opening career opportunities to people who can master the language, there is also an increase of interest on not only the language but also on how to teach the language. Teachers in this context the teachers of Mandarin language at least master the basic of teaching Mandarin language in the five elements. On this matter, there are errors in the writing of Han Characters that occurs during the learning process on learners and teachers. The errors cover the strokes misalignment on written texts of describing oneself. This study analyzes the common mistakes of Chinese stroke writing on Teachers of Chinese language teachers in Indonesia on training programmes.
 Key Words: Han Characters; Error Analysis in writing; writing misalignment; Chinese stroke writing and Mandarin Language Teachers.

Study on themisprints of the primary version of the Compendium of Materia Medica

The Jinling version of the Compendium of Materia Medica was the primary version.It accurately represented the ideas of LI Shizhen, therefore it was valued by the scholars.There were many defects in misprint (knife carving) and proofreading.The main errors were some Chinese character's strokes missing and wrong characters printed.The authors collected some statistics data andmade some statistical analysis. They found that total misprints were 376 and emerge 615 times.Those misprints can divide into two categories: lack of some parts of Chinese characters; lack of some strokes of Chinese characters.Four tables which list the misprints was made.It is helpful for scholars to examine Compendium of Materia Medica or the study of other similar ancient literatures.

THE NATURE OF THE CHINESE SCRIPT

This paper introduces the nature of the Chinese script, with the focus on its origin and development, the main four kinds of creation methods, the basic strokes of Chinese characters and the significance of its radical structure. The author believes that Chinese characters are the carriers of the script, phoneme and meaning as well as of the grammatical structure of the Chinese language. Hence, the Latin alphabet for the Chinese phonetic system cannot replace Chinese character, and learning Chinese character is very important for foreign learners. Finally, the author gives a mnemonic of the basic rules for stroke order that facilitates learners' memory and helps them write characters correctly and quickly.

Eye-movement evidence for the mental representation of strokes in Chinese characters.

Although strokes are the smallest identifiable units in Chinese words, the fact that they are often embedded within larger units (i.e., radicals and/or characters that comprise Chinese words) raises questions about how and even if strokes are separately represented in lexical memory. The present experiment examined these questions using a gaze-contingent boundary paradigm (Rayner, 1975) to manipulate the parafoveal preview of the first of two-character target words. Relative to a normal preview, the removal of whole strokes was more disruptive (i.e., resulting in longer looking times on targets) than the removal of an equivalent amount of visual information (i.e., number of pixels) from strokes located either in similar locations or throughout the entire character. These findings suggest that strokes are represented as discrete functional units rather than visual features or integral parts of the radicals/characters in which they are embedded. We discuss the theoretical implications of this conclusion for models of Chinese word identification. (PsycINFO Database Record (c) 2019 APA, all rights reserved).