A Review of Research on Handwritten Chinese Character Recognition with Multi-Feature Fusion

This paper analyzes the progress of handwritten Chinese character recognition technology,from two perspectives:traditional recognition methods and deep learning-based recognition methods.Firstly,the complexity of Chinese character recognition is pointed out,including its numerous categories,complex structure,and the problem of similar characters,especially the variability of handwritten Chinese characters.Subsequently,recognition methods based on feature optimization,model optimization,and fusion techniques are highlighted.The fusion studies between feature optimization and model improvement are further explored,and these studies further enhance the recognition effect through complementary advantages.Finally,the article summarizes the current challenges of Chinese character recognition technology,including accuracy improvement,model complexity,and real-time problems,and looks forward to future research directions.

A 4-Corner Codes Classifier Based on Decision Tree Inductive Learning for Handwritten Chinese Characters

The classification for handwritten Chinese character recognition can be viewed as a transformation in discrete vector space. In this paper, from the point of discrete vector space transformation, a new 4-corner codes classifier based on decision tree inductive learning algorithm ID3 for handwritten Chinese characters is presented. With a feature extraction controller, the classifier can reduce the number of extracted features and accelerate classification speed. Experimental results show that the 4-corner codes classifier performs well on both recognition accuracy and speed.

A Study on the Effect of Creative Chinese Characters Learning on the Ability to Learn Chinese Old Sayings and Idioms Using K-Pop Music Video

Music is an extraordinary bridge between people all over the world so much as to be called a universal language. Idols and B-boys stages are fun, touching, and fantastic. Today, South Korean students are excited and enthusiastic about their colorful dance moves. The study is about creative educational methods that use K-pop music videos to learn the proverbs and old words that our ancestors learned to keep in mind and teach. K-pop lyrics are a rich reflection of the experiences of life and the world in which people are living today. Accordingly, this study can present new teaching and learning method examples that are used in class related to the old language associated with K-pop lyrics and can also introduce interesting class materials.

Chinese word segmentation with local and global context representation learning

A local and global context representation learning model for Chinese characters is designed and a Chinese word segmentation method based on character representations is proposed in this paper. First, the proposed Chinese character learning model uses the semanties of loeal context and global context to learn the representation of Chinese characters. Then, Chinese word segmentation model is built by a neural network, while the segmentation model is trained with the eharaeter representations as its input features. Finally, experimental results show that Chinese charaeter representations can effectively learn the semantic information. Characters with similar semantics cluster together in the visualize space. Moreover, the proposed Chinese word segmentation model also achieves a pretty good improvement on precision, recall and f-measure.

Incorporating Linguistic Rules in Statistical Chinese Language Model for Pinyin-to-character Conversion

An N-gram Chinese language model incorporating linguistic rules is presented. By constructing elements lattice, rules information is incorporated in statistical frame. To facilitate the hybrid modeling, novel methods such as MI-based rule evaluating, weighted rule quantification and element-based n-gram probability approximation are presented. Dynamic Viterbi algorithm is adopted to search the best path in lattice. To strengthen the model, transformation-based error-driven rules learning is adopted. Applying proposed model to Chinese Pinyin-to-character conversion, high performance has been achieved in accuracy, flexibility and robustness simultaneously. Tests show correct rate achieves 94.81% instead of 90.53% using bi-gram Markov model alone. Many long-distance dependency and recursion in language can be processed effectively.

阅读伴随词汇学习的词切分:首、尾词素位置概率的不同作用

本研究通过两个平行实验,探讨重复学习新词时首、尾词素位置概率信息作用于词切分的变化模式。采用阅读伴随词汇学习范式,将双字假词作为新词,实验1操纵首词素位置概率高低,保证尾词素相同;实验2操纵尾词素位置概率高低,保证首词素相同。采用眼动仪记录大学生阅读时的眼动轨迹。结果显示:(1)首、尾词素位置概率信息的词切分作用随新词在阅读中学习次数的增加而逐步变小,表现出“熟悉性效应”。(2)首词素位置概率信息的“熟悉性效应”表现在回视路径时间、总注视次数两个相对晚期的眼动指标,而尾词素位置概率信息的“熟悉性效应”则从凝视时间开始,到回视路径时间,再持续到总注视时间。结果表明首、尾词素的位置概率信息均作用于阅读伴随词汇学习的词切分,但首词素的作用时程更长,更稳定,支持了首词素在双字词加工中具有优势的观点。

电打汉字开路,笔写汉字跟进——革新国际中文教育中的汉字教学模式

汉语和英语作为外语教学有相同之处,更有相异之处。那相异之处突出体现在中国人学英语,一般来说,口语学习与书面语的学习几乎是同步的,从口语进入书面语学习无需在文字学习上下很大功夫,花很多时间;可是英语区学生学习汉语,其口语学习与书面语的学习无法同步,他们从口语学习进入书面学习,必须在汉字学习上再要下功夫、花很多时间,过好“汉字关”。汉语老师如何帮助学生过好“汉字关”?在如今的智能时代需要考虑革新汉字教学模式,采用“电打汉字”的汉字教学新模式;其具体实施策略是“电打汉字开路,笔写汉字跟进”。如何“电打汉字开路,笔写汉字跟进”?文章提供了具体的教学措施。

基于Swin Transformer和CNN的汉字书法教学系统

针对日益增长的汉字书法学习需求,将滑动窗口自注意力(Swin Transformer,ST)模型和卷积神经网络(Convolutional Neural Network,CNN)模型相结合,提出手写体汉字识别ST-CNN模型,进而开发了汉字书法教学系统。实测结果表明,ST-CNN模型识别准确率约为91.6%,较传统的ST模型提升了约0.5个百分点,较传统的CNN模型与ST模型,在收敛速度上分别提升了约10和30个百分点,开发的汉字书法教学系统性能良好。

“手写”与“电写”对汉字学习的影响及相关研究视角

随着数字技术与人工智能技术的飞速发展,计算机、智能手机等电子设备已成为日常生活、工作与学习不可或缺的一部分,从执笔书写到“电写”的变革减轻了学习者汉字学习的难度,提升了信息处理的效率,也深刻地改变了汉字学习方式与教学方式。通过探讨在从执笔书写方式向“电写”方式转变的过程中,“手写”与“电写”对汉字学习方式及汉字教学模式产生的深远影响,并分析了“电写”的利与弊,同时也从不同的维度提出了“电写”时代应该关注和研究的问题,呼吁不同学科对汉字认知的相关问题进行交叉实证研究。

中国学视域下的国际中文教育知识体系构建

中国学是历史中国之学,也是当代中国之学。世界中国学因为有了中国的参与,使其自身由“外国学”而更新迭代为“文明互鉴之学”。中国学的孕育和诞生,离不开中文的教育与学习。中国学是中外文化交流的果实,而交流的起始就是语言的互通。中国学是国际中文教育的战略性先导,国际中文教育是中国学蓬勃发展的基础性支撑。未来国际中文教育的知识体系构建,应该着眼于中华文化的世界传播,聚焦于中国特色哲学社会科学话语体系的构建,服务于世界文明的交流互鉴,从加强中文书面语教学、构建中文教育学学科和强调中文的文化交流属性入手,使国际中文教育在文明互鉴的时代能够不断创新发展并行稳致远。

“余”字本义的“帝自称说”汉字字源学考辩——基于“炎黄崇拜”假说

“余”字本义释义存在多种解说。但究竟哪种解说合乎逻辑思辨和推理的科学性标准,需要进行汉字字源学层面的考证辨析。该文提出“余”字本义的“帝自称说”,借助“炎黄崇拜”假说的理论工具,从“余”字字族、“亼”字字族、专有名称、专有地名、专有自称5个维度进行了系统的考辩,得出“余”应为上古华夏文明中帝对自己的专有称谓,“余”字造字构型应当从亼从木。其中“亼”字字根代表了“△”形帝陵、神庙或神坛,是“炎黄崇拜”的标志性建筑形制;“木”字字根为“炎黄”二帝来自神授之权杖的简体造型。该文的探索性研究旨在为“余”字本义的“帝自称说”提供有力论证的同时,亦能够验证“炎黄崇拜”假说在建构汉字字源学理论体系中的有效性。

汉字进阶课程翻转课堂教学模式实践探索

汉字进阶课程是面向具有中高级汉语水平的留学生开设的一门新型课程,旨在揭示汉字的系统性、规律性和文化性特征,帮助留学生打破汉语学习的瓶颈。在课程实践探索过程中,借鉴翻转课堂教学模式的前沿发展成果,在时序重构的基础上进一步优化翻转课堂的教学模式,促进更有效的师生互动和真正以学生为中心的自主学习和深度学习。结果表明,汉字进阶课程的特点与翻转课堂的教学理念高度契合,通过教材建设、录制课前学习资源、优化课堂教学模式、调整课后学习任务、构建教学支架等尝试,能有效推进教学内容、实现教学目标、提升课程价值。

The comparison between traditional Chinese literacy strategies and English vocabulary strategies

There is a positive transfer from native language vocabulary learning strategy to that of the second language. The comparison between them shows that the traditional Chinese character learning strategies have profound effect on English vocabulary learning on the basis of morphology, lexicon as well as discourse categories. If the mutual effect can be applied in English vocabulary learning effectively, positive transfer emerges.

基于OCR技术的档案智能化收集方法研究

为实现档案信息的智能化管理,文章提出了一种轻量化的端到端档案智能化收集系统。首先采用轻量化的目标检测神经网络PP-PicoDet作为布局检测器,用于对档案材料的版面分析;然后采用SLANet深度学习神经网络进行表格的结构化识别;最后使用开源的Paddle OCR引擎进行文本识别。系统对表格识别的准确度达到75.8%,印刷体文本识别准确度达到98.3%,总推理时间少于0.85 s。该系统为实现端到端的档案资料智能化收集,提高档案资料整理的效率提出了一种有效解决方案。

在真实情境中实现自主积累与初步梳理——第一学段“语言文字积累与梳理”学习任务群教学实施与建议

“语言文字积累与梳理”是基础型学习任务群,直接指向语文核心素养的“语言运用”维度。在第一学段的教学中,教师要引导学生通过体验、发现、运用等逐步了解和掌握语言文字运用规律,感受丰富的汉字文化,进而促进学生核心素养的提升。

韩国汉字教学现状——兼谈移动学习(M-Learning)与汉字识字教学法的运用

随着网络市场急速发展，智能产品市场扩张，韩国教育的发展趋势也发生变化。教育市场的需求使人们关注教育和游戏相结合的教育游戏（edutainment），并结合多媒体教育和智能产品研制开发相关教育软件，取得了很大的成果。韩国汉字教育也受到教育环境变化的影响，向虚拟化、智能化的方向发展。本文通过对网上教学与移动学习方面的汉字教学资源如网上汉字教学、汉字游戏、汉字学习应用程序的分析，考察目前韩国网络环境下的汉字教学情况。同时以汉字学习应用程序为例，讨论适用于汉字移动学习的汉字教学法。目前韩国汉字学习应用程序只限于提示汉字的读音及意义，多采用问答（Quiz）形式测试已学过的字。本文在汉字学理论及汉字教学理论的基础上，探讨具有层次性及多元化的适用于汉字移动学习的汉字教学法。

Processing Chinese hand-radicals activates the medial frontal gyrus A functional MRI investigation

Embodied semantics theory asserts that the meaning of action-related words is neurally represented through networks that overlap with or are identical to networks involved in sensory-motor processing. While some studies supporting this theory have focused on Chinese characters, less attention has been paid to their semantic radicals. Indeed, there is still disagreement about whether these radicals are processed independently. The present study investigated whether radicals are processed separately and, if so, whether this processing occurs in sensory-motor regions. Materials consisted of 72 high-frequency Chinese characters, with 18 in each of four categories： hand-action verbs with and without hand-radicals, and verbs not related to hand actions, with and without hand-radicals. Twenty-eight participants underwent functional MRI scans while reading the characters. Compared to characters without hand-radicals, reading characters with hand-radicals activated the right medial frontal gyrus. Verbs involving hand-action activated the left inferior parietal lobule, possibly reflecting integration of information in the radical with the semantic meaning of the verb. The findings may be consistent with embodied semantics theory and suggest that neural representation of radicals is indispensable in processing Chinese characters.

CTSF:An End-to-End Efficient Neural Network for Chinese Text with Skeleton Feature

The past decade has seen the rapid development of text detection based on deep learning.However,current methods of Chinese character detection and recognition have proven to be poor.The accuracy of segmenting text boxes in natural scenes is not impressive.The reasons for this strait can be summarized into two points:the complexity of natural scenes and numerous types of Chinese characters.In response to these problems,we proposed a lightweight neural network architecture named CTSF.It consists of two modules,one is a text detection network that combines CTPN and the image feature extraction modules of PVANet,named CDSE.The other is a literacy network based on spatial pyramid pool and fusion of Chinese character skeleton features named SPPCNN-SF,so as to realize the text detection and recognition,respectively.Our model performs much better than the original model on ICDAR2011 and ICDAR2013(achieved 85%and 88%F-measures)and enhanced the processing speed in training phase.In addition,our method achieves extremely performance on three Chinese datasets,with accuracy of 95.12%,95.56%and 96.01%.

基于改进卷积神经网络的手写汉字识别研究

手写汉字识别是智能信息化的重要研究领域之一,在手写输入、文件录入等相关领域至关重要。然而,由于汉字的种类繁多、结构复杂等问题,通常的深度学习方法对汉字的特征提取能力差、计算量大,汉字识别的准确度无法达到令人满意的程度。为解决上述问题,本研究提出了一种改进的深层卷积神经网络模型,在模型的结构上添加了批标准化层,在损失函数上加入了Dropout和正则化方法,在训练过程中加入了RMSprop优化器。为了证实提出的模型的有效性,在CASIA-HWDB1.1和IAHCC-UCAS2016的数据集上进行实验。对提出的模型进行了整体实验,与其他深度学习模型进行了分组对比实验,对批标准化层和RMSprop优化器进行了消融实验,全方面验证了本研究模型的高准确率和高运行速度。

基于直观汉字构形原理的C^(3)-GAN字体生成优化方法

目的为了提升生成对抗网络汉字风格迁移的图像生成质量,实现汉字智能生成在字库产业中的实际应用,提出了一种基于直观汉字构形学的条件生成对抗网络字体生成优化方法(Optimizationof Conditional Fonts Generation with Chinese Character Configuration GANs,C^(3)-GAN)。方法建构了直观汉字构形模组(C^(3)Module),该模组包含了利于条件生成对抗网络进行汉字构形语义特征学习的全特征汉字字符集。C^(3)-GAN在条件生成对抗网络模型下进行字体生成训练,降低了必要训练样本数量,实现对字体生成效果的优化。结果使用C^(3)-GAN生成汉字图像的清晰度更高、字形更准确。在图像相似性定量评估中,使用C^(3)-GAN的实验组相比于其他模型,获得了更高的相似值和更小的误差值。结论使用C^(3)-GAN可以降低必要训练样本数量、提升汉字图像质量。在实际项目中具有一定的应用性和可操作性。