Character Features Research Articles

Integrating path signature and pen-tip trajectory features for online handwriting Yi text recognition

Recognizing online handwriting Yi text is crucial for recording and preserving Yi literature. However, the scarcity of online handwriting Yi text datasets has limited relevant research, impeding the process of Yi informatization. In this work, we use synthetic data to train models, and an Online Handwriting Yi Text Recognition Network (YTRN) is proposed, which extracts robust character features to address the gap between synthetic and real data. YTRN adeptly learns the spatial structure features from path signature feature maps and captures trajectory features from the pen-tip trajectories. Subsequently, an innovative adaptive feature fusion module integrates these two sets of features to yield more comprehensive and robust character representations. Experiments on our newly collected Yi-OLHWDB2.0 dataset demonstrate that our method outperforms previous approaches, achieving an impressive 95.67% accuracy. This highlights the model’s effectiveness in extracting comprehensive and robust features from path signature maps and pen-tip trajectories, significantly enhancing recognition accuracy and generalization.

Rancang Bangun Aplikasi Edukasi Reproduksi Manusia Pada Pelajar SMPN 187 Jakarta Berbasis Android

The implementation of education at SMPN 187 Jakarta is often limited to the use of PPT and RPP,causing the material to become monotonous and less interesting for students. Reproductioneducation is also often not delivered comprehensively, resulting in less than optimal studentunderstanding. To overcome this, an Android-based application was developed which aims toincrease students' understanding of reproductive health. This application provides educationalmaterial covering secondary and primary aspects as well as a test feature to measure studentunderstanding, making learning more effective and interactive. This application uses Flutter as amulti-platformframework andDart as an open source programming languagewithOOPconceptsthat are easy to understand. The application backend is managed with Laravel, functioning asBackendAPImanagement of themobile application.The prototypemethod is used in development,starting from collecting user needs to creating a design/prototype. The results of this research areas an educational application and learning support media for students at SMPN 187 Jakarta bymaking learning media flexible and efficient, with features for characters that help improvestudents' and female students' understanding of educational reproduction.

Vladimir Igorevich Kolpikov: Portrait of a Siberian architect

The article is devoted to the Irkutsk period of life and work of the outstanding Soviet and Russian architect Vladimir Igorevich Kolpikov (1936-2005), chairman of the Irkutsk branch of the Union of Architects of the USSR (1968-1975). It presents new facts and information about his biography, his professional formation, his extensive practical work experience in various spheres, including co-authorship with prominent masters of the 1960-1970s. The article reveals the emerging interest in the search for his creative path, active attitude to life and features of the Siberian character of the architect and artist. The list of his projects and buildings in Irkutsk and the Irkutsk region is specified.

Pose‐to‐Motion: Cross‐Domain Motion Retargeting with Pose Prior

AbstractCreating plausible motions for a diverse range of characters is a long‐standing goal in computer graphics. Current learning‐based motion synthesis methods rely on large‐scale motion datasets, which are often difficult if not impossible to acquire. On the other hand, pose data is more accessible, since static posed characters are easier to create and can even be extracted from images using recent advancements in computer vision. In this paper, we tap into this alternative data source and introduce a neural motion synthesis approach through retargeting, which generates plausible motion of various characters that only have pose data by transferring motion from one single existing motion capture dataset of another drastically different characters. Our experiments show that our method effectively combines the motion features of the source character with the pose features of the target character, and performs robustly with small or noisy pose data sets, ranging from a few artist‐created poses to noisy poses estimated directly from images. Additionally, a conducted user study indicated that a majority of participants found our retargeted motion to be more enjoyable to watch, more lifelike in appearance, and exhibiting fewer artifacts. Our code and dataset can be accessed here.

Enhanced license plate recognition using deep learning and block-based approach

This study investigates the effectiveness of current deep learning techniques in license plate detection and makes essential contributions. Instead of classifying the characters on Turkish license plates with a single classifier, the characters are divided into blocks of numbers and letters using various image processing techniques, and a separate classifier is used for each block. The proposed approach improves character classification accuracy and license plate recognition accuracy. This approach eliminated the possibility of misclassifying similar letters and numbers and improved the character classification accuracy from 95.9% to 99.6%. In addition, a new character feature dataset was created, and a deep learning model was trained on this dataset. Integrating this model into the system increased the classification accuracy to 99.7%. The YOLOv8 object detection model, trained using CUDA technology, achieved a mAP of 98.9%. The overall accuracy of the whole system in license plate recognition reached 97.3%. This study proves the effectiveness of current deep learning methods and the proposed block-based character recognition approach in license plate recognition.

ARTHUR SCHNITZLER’S DRAMA “DER GRÜNE KAKADU”: PROBLEMATICS AND POETIC MANNER

The article deals with Arthur Schnitzler’s one act drama “Der grüne Kakadu”. Numerous questions were raised in this small in volume play thanks to the genre of one act play in which the dialogue is the leading ground of dramatic action as well as theatre within a theatre structure. The structural dramatic element is the story lines parallelism, the most important problems of Arthur Schnitzler’s work such as love and death, reality and illusion, truth and lie, play and actuality are covered from various perspectives. It has been proved that the confusion of reality and illusion helps to bring the features of characters to light – they represent only the theatrical discourse and are not able to realise their potential in real life. The aesthetic enjoyment of playing gets the negative connotation and makes for self-delusion and inability to find the place in real life. Thanks to translocation of real and theatrical the distance between the stage and audience are also reduced resulting in the reception problem proceeded by the offstage spectator as the main subject. The following approach to the creation of a dramatic work has become more and more relevant in the literature of the 20th century.

Functional interactions underlying visuospatial orthographic processes in Chinese reading.

As a logographic writing system, Chinese reading involves the processing of visuospatial orthographic (ORT) properties. However, this aspect has received relatively less attention in neuroimaging research, which has tended to emphasize phonological (PHO) and semantic (SEM) aspects in processing Chinese characters. Here, we compared the functional correlates supporting all these three processes in a functional MRI single-character reading study, in which 35 native Chinese adults were asked to make ORT, PHO, and SEM judgments in separate task-specific activation blocks. Our findings revealed increased involvement of the right hemisphere in processing Chinese visuospatial orthography, particularly evident in the right ventral occipito-temporal cortex (vOTC). Additionally, time course analysis revealed that the left superior parietal gyrus (SPG) was initially involved in SEM processing but contributed to the visuospatial processing of words in a later time window. Finally, ORT processing demonstrated stronger recruitment of left vOTC-SPG-middle frontal gyrus (MFG) functional connectivity compared to SEM processing. This functional coupling correlated with reduced regional engagement of the left vOTC and MFG, highlighting that visuospatial ORT processes in reading Chinese rely on functional interactions among key regions rather than local regional processes. In conclusion, these findings underscore visuospatial ORT processes as a distinctive feature of reading logographic characters.

Transformer-based end-to-end attack on text CAPTCHAs with triplet deep attention

Websites frequently use text-based captcha images to distinguish whether the user is a person or not. Previous research mainly focuses on different training strategies and neglects the characteristics of the text-based captcha images themselves, resulting in low accuracy. For text-based captcha images characterized by rotation, distortion, and non-character elements, we propose an end-to-end attack using a Transformer-based method with triplet deep attention. Firstly, the features of text-based captchas are extracted using ResNet45 with triplet deep attention module and Transformer encoder. The TDA module is capable of learning rotational and distortion features of characters. Subsequently, based on self-attention mechanism, design query, key, and value, and adopt the query enhancement module to enhance the query. The query enhancement module can strengthen character localization and reduce attention drift towards non-character elements. Finally, the feature maps are transformed into probabilities of character for the final text recognition. Experiments are conducted on captcha datasets based on Roman characters from 9 popular websites, achieving average word accuracy of 91.14%. To evaluate the performance of our method on data with small samples, experiments are conducted different scales of training data. Additionally, we use the method on Chinese text-based captcha tasks and achieve average word accuracy of 99.60%. The effectiveness of the method is also explored under conditions of lack of illumination and scene text recognition, where background interference is present.

The interactive fusion of characters and lexical information for Chinese named entity recognition

Many studies have demonstrated that incorporating lexical information into characters can effectively improve the performance of Chinese Named Entity Recognition (CNER). However, we argue that previous studies have not extensively explored the interactive relationship between characters and lexical information, and have only used the lexical information to enhance character-level representation. To address this limitation, we propose an interactive fusion approach that integrates characters and lexical information for CNER. Specifically, we first design graph attention networks to initially fuse character and lexical information within an interactive graph structure. Additionally, by introducing methods such as feedforward neural networks, residual connections, and layer normalization, the fusion effect of the graph attention network is further enhanced. Finally, concatenating and reducing dimensionality of character feature vectors and lexical feature vectors to achieve secondary fusion, thereby obtaining a more comprehensive feature representation. Experimental results on multiple datasets demonstrate that our proposed model outperforms other models that fuse lexical information. Particularly, on the CCKS2020 and Ontonotes datasets, our model achieves higher F1 scores than previous state-of-the-art models. The code is available via the link: https://github.com/wangye0523/The-interactive-fusion-of-characters-and-lexical-information-for-Chinese-named-entity-recognition.

The Historicization and Theorization of Spatial Ruin in Wounded Baghdad

Be it a consideration of the ruptured spatialities and urban forms of Baghdad in the present or reaching back into a volatile past, the city lives out arresting contradictions that beckon a different kind of understanding. Spatial woundedness carries within it many implications for how that pain is rendered. This is most visible in forms of cultural production that have settled the hurt exacted into a place of innovation. It is a full mutuality between architecture and the destruction that has violently defaced it so much so that a new image of urbanity begins to emerge. Beyond the agony felt by the capital, intellectualizing the spatial wound that has manifested and been given life as it were suggests a pressing need to comprehend this phenomenology. Historicizing and then theorizing what is a poetics of destruction assists in coming to comprehend not simply the features of an urban character necessarily born out of ruin but doing so against an orientalist narrative that seeks to entrench the Baghdadi space in perpetual liminality. The city fights back so that it may have a chance to survive but also creates lasting meaning between the real and unreal.

Novel Deep Learning Application: Recognizing Inconsistent Characters on Pharmaceutical Packaging

Background Machine vision faces significant challenges when applied to text recognition on cardboard packaging particularly due to multiple printing methods, irregular character shapes, and curved packaging surfaces. Methods This research introduces a novel deep learning application for recognizing binarized expiration date and batch code characters printed using multiple printing methods. The method, based on Region-based Convolutional Neural Networks (R-CNN), enables character recognition directly from in the images without the need for extracting handcrafted features. In detail, this approach performs character recognition by using the whole image as input, extracting and learning salient character features directly from the packaging surface images. Results The R-CNN model, with a precision of 91.1% and an F1 score of 80.9%, effectively recognizes manufacturing markings on pharmaceutical packages, with inconsistencies in the characters’ shapes. In a comparative experiment using the same dataset of images, the R-CNN model significantly outperformed Tesseract OCR, achieving much higher precision, recall, and F1 scores. Conclusions The results of this study reveal that the deep learning method outperforms the well-established optical character recognition method in recognizing text characters printed with different printing methods. Presented in this study, the deep learning method recognizes text characters with high precision. It is also suitable for recognizing text printed on curved surfaces, provided proper preprocessing is applied. The problem investigated in the study differs from previous research in the field, focusing on the recognition of texts printed with different printing methods. The research thus fills a gap in text recognition that existed in the research of the field. Furthermore, the study presents new ideas that will be utilized in our future research.

STAGES OF THE ANALYSIS OF RELIGIOUS VOCABULARY IN THE AMERICAN PRESIDENTIAL DISCOURSE

The article is devoted to a review of the methodology for analyzing the functioning of religious vocabulary in the American presidential discourse. The work defines the political discourse, distinguishes the genres of the American presidential discourse, develops an algorithm for their research, and highlights the methods of analysis. One of the types of institutional discourse is political discourse, which is a reflection of the socio-political life of the country, contains elements of its culture, as well as features of the national character, general and national-specific cultural values, and aims to gain and maintain political power. The main functions of political discourse are informative, instrumental, prognostic, normative, legitimating, persuasive and political propaganda. The genre space of the presidential discourse is determined by the general political system of the state, the historical and political traditions of the institution of the presidency, and the sociocultural conditions for the existence of political discourse. These extralinguistic factors, as well as the nature of the main intention, allow us to distinguish such genres of US presidential discourse as ritualistic, orientational, and agonal. In the linguistic literature, there is a large number of classifications of political discourse strategies. In our work, we adhere to the classification according to which the following communicative strategies are inherent in political discourse: self-presentation, discrediting, manipulation, self-defense, information-interpretive, forming the emotional mood of the addressee, argumentative strategy, agitation. Each of these strategies is represented by a number of tactics. The search for effective ways of analyzing and describing the semantics and function of religious vocabulary in the American presidential discourse determined the choice of the main approaches to the research object. The methods used in linguistic studies involve the implementation of a system of procedures for studying the object of research and verification of the obtained results. Typical guidelines of systematic philological analysis include paying attention to the semantic, substantive side of the text, represented by text categories, and to the communicative immersion of the text in discursive reproduction. For a systemic complex analysis, the work involves such methods that take into account the communicative essence of the discourse text, reflect the significance of the text in the light of its generation and interpretation.

COSMOLOGICAL CONCEPT OF TIME IN “THE CHANGE OF ZHOU” (“THE BOOL OF CHANGE”)

This paper represents one chapter from my PhD dissertation, written and successfully defended at Beijing Normal University in 2012. The whole content is translated from my doctoral dissertation that was originally written in Chinese language. According to the existing references, the earliest Chinese philosopher who mentioned in his essays the word “cosmos – yuzhou” was Shi Jiao (390-330 BC). He said: “four directions of Heaven and Earth are called ′yu′, time since ancient times till today is called ′zhou′”. In other words, yu represents space, while zhou symbolizes time, and that is why Chinese ancient cosmology may be even called “concept of space and time”. Inscriptions on the oracle bones and tortoiseshells with the symbols called “ganzhi”, originating from the Yin dynasty, were used to record hours, days, months and years i.e. time, but at the same time, Chinese people used these symbols to mark the directions of space, too. All of the cosmological diagrams used by “The Change of Zhou” and yijingologists, like Fuxi’s transcendental bagua diagram, king Wen’s empirical bagua diagram, river map and Luo book simultaneously depict time and space. In the eyes of the Chinese people, since ancient times, time and space are one whole and inseparable unity. However, being a foreigner, the writer of these lines will separately analyze the meaning of time in “The Book of Change”. As a foreigner, I find that the greatest charm of Chinese characters is that every character holds the whole history and etymology of one word or term in it. In other words, the most prominent feature of Chinese characters is symbolism. Therefore, if we want to explore “the time” concept, we must start from the character 时- shi meaning “time”. Character shi is formed of one radical meaning “sun”, and another meaning “measure”. This shows that the meaning of character shi is “measuring the sun”, i.e. measuring the length of sun shadow. The shorter the sun shadow is, the weather is being warmer and the Yang energy gets more abundant. It explains that character shi depicts increasing and decreasing of Yin and Yang energy in the cosmos and on the Earth. This paper analyzes time concept in “The Change of Zhou” from the Chinese and Western point of view. It explains meanings of Heavenly dao, Heavenly time, chance and synchronicity. The paper uses time theories (like simplicity of time, presentism, etc.) of German scientist Leibniz（1646 – 1716）, and famous Swiss psychologist Carl Jung’s（1875 - 1961） concepts (like cosmical archetypes and synchronicities), and compare them with ideas of “the cognition of subtle”, “progress through change” and “capability to grasp the chance” in “The Change of Zhou”. Finally, it combines and summarises the concept of time from “The Change of Zhou” and Western point of view.

Geopsychic reactions and nature of the Ukrainian

In a content-rich and structurally compact study of the worthy Ukrainian patriot, Professor Ivan Rybchyn (1892 – 1970) one of the most fundamental features of the Ukrainian character and at the same time the brightest aspects of the national spirit is highlighted – the primary influence of geopsychological factors (native nature, land, landscape) on the formation of a colorful, the original and contradictory harmonized character-destiny of a Ukrainian. It has been convincingly proven that precisely the geopsychic reactions, being caused by the geographical environment-landscape, determine one or another, in details and features, the course of psycho-spiritual processes, states, properties, tendencies of a person, which mutually form what is called character, disposition, soul. The author, in a fully reasoned way, singles out and characterizes the f o u r forms of dependence of the last odes of the Ukrainian lands’ spatial-relief invariants, motivatedly arguing that the mental e x p e r i e n c e s of Ukrainians: a) from the lower steppe strip are imaginatively enriched with dynamic material into an infinite distance, more deeply diversified to feel the majestic and unlimited, expanded by intense emotionally and effectively through a shift of attention to the absence of external thought motives and amplifying the volitional potential of the steppes channels for the development of dispositions to explosive deed actions, which are distinguished by high dynamics of mental stress and short-term prolongation; b) from the strips of forest-steppe highlands, thanks to the softness and gentleness of the wavy line of the landscape, stimulate and direct the development of the disposition in the Ukrainian ethnic group towards the mood of cheerfulness and the complementarity of rational and aesthetic guidance in the existential affirmation of everyday life; c) from the strips of forested northern and northwestern lowlands, mainly because of the darkness of the forest thickets, establish and consolidate dispositions for learning in the mystery of forest nature and intimately friendly attitude to the forest as their faithful defender in the struggle for the survival and prosperity of the people; d) from the mountain environment of the Carpathians and other hills contribute to the compliance, cheerfulness and willingness to indulge the uncontrolled intensity of the explosions of passions and, in addition, the play of light and shadows and the colorfulness of the changing mountain landscapes inherit the corresponding aesthetic dispositions.

Особенности национального характера в романах М. А. Шолохова

The features of the national character in the prose of M. A. Sholokhov («Quiet Flows the Don», «Virgin soil upturned», «They Fought for Their Country») are considered. The main purpose of the study is to show the evolution of national character in the work of M. A. Sholokhov, to determine the content of the concept of «national character» and the criteria for its formation, the conditions for its change under the influence of historical events. The most vivid images of Cossack heroes are analyzed in connection with folk beliefs, folklore traditions, national life and the originality of the Don nature

CFNAM-PG: Bridging Phonetic and Glyphic Information for Chinese Full Name and Abbreviation Matching Based on Simbert and DenseNet

Matching abbreviated names with their full names (full-abbr matching) plays a key role in data integration, address matching, information retrieval, and other fields. Traditional full-abbr matching technology often encounters issues related to near homophones and near homoglyphs. First, a near-homophone full-abbr matching model based on Simbert and VGG was first proposed, which integrates character and speech features, leveraging a speech recognition model and combining a brain-like cognitive learning dual-process mechanism which involves linguistic knowledge and neural network together. Second, to address the problem of near-homoglyph full-abbr matching in Chinese, a DenseNet-based model that fuses glyph structure and image features was proposed, in which statistical feature extractors are employed to extract feature vectors for glyphic features including stroke, Wubi and structural features separately. Lastly, the near-homophone model and the near-homoglyph model are coupled to work together in the full-abbr matching task, in which expert knowledge is used as a component of the feature optimizer. Experimental results showed that the integrated model significantly increased the matching accuracy to 87.5%, demonstrating a 12.3% improvement.

A Benchmark for Morphological Segmentation in Uyghur and Kazakh

Morphological segmentation and stemming are foundational tasks in natural language processing. They have become effective ways to alleviate data sparsity in agglutinative languages because of the nature of agglutinative language word formation. Uyghur and Kazakh, as typical agglutinative languages, have made significant progress in morphological segmentation and stemming in recent years. However, the evaluation metrics used in previous work are character-level based, which may not comprehensively reflect the performance of models in morphological segmentation or stemming. Moreover, existing methods avoid manual feature extraction, but the model’s ability to learn features is inadequate in complex scenarios, and the correlation between different features has not been considered. Consequently, these models lack representation in complex contexts, affecting their effective generalization in practical scenarios. To address these issues, this paper redefines the morphological-level evaluation metrics: F1-score and accuracy (ACC) for morphological segmentation and stemming tasks. In addition, two models are proposed for morpheme segmentation and stem extraction tasks: supervised model and unsupervised model. The supervised model learns character and contextual features simultaneously, then feature embeddings are input into a Transformer encoder to study the correlation between character and context embeddings. The last layer of the model uses a CRF or softmax layer to determine morphological boundaries. In unsupervised learning, an encoder–decoder structure introduces n-gram correlation assumptions and masked attention mechanisms, enhancing the correlation between characters within n-grams and reducing the impact of characters outside n-grams on boundaries. Finally, comprehensive comparative analyses of the performance of different models are conducted from various points of view. Experimental results demonstrate that: (1) The proposed evaluation method effectively reflects the differences in morphological segmentation and stemming for Uyghur and Kazakh; (2) Learning different features and their correlation can enhance the model’s generalization ability in complex contexts. The proposed models achieve state-of-the-art performance on Uyghur and Kazakh datasets.

VI SUAL AND NOMINATIVE SIGNS OF CHARACTERS IN MARI NON-FABULOUS PROSE

: The text considers the visual appearance of demonic characters in the modern Mari folklore. Based on the texts of non-fabulous prose (bylichka, byvalschina), the author explores the manifestations of otherness in the descriptions of “evil spirits”: iya, keremet, ovda, targyltysh, revenants. The research focuses on models of describing the images of the Mari evil spirits, their visual characteristics and liminality in folklore narratives. The author considers the categories of color, size and other attributes as characteristics marking demonic folklore characters as chthonic. The work is based on field materials collected in places of compact residence of the Mari population in 2017–2022 (the Mari El Republic, the Kirov oblast’, the Republic of Bashkortostan). The work was carried out in an interdisciplinary manner, based on a combination of ethnographic and folkloristic scientific methods. The main scholarly direction addressed by the researcher is a semiotic approach to the study of myth and folklore, that is, a view of folklore texts and images as a sign system of concepts. The author combines the methods of field research, interviewing, narrative analysis, as well as the typological method. According to the author, in the description of “evil spirits” by informants several models of representation can be distinguished: anthropomorphic, anthropomorphic with hyperbolized features, zoomorphic, abstract. Special attention in the article is paid to the nominative features of characters in oral texts. The res earcher noted a tendency of informants to neutralize category of a folklore character.

Chinese Named Entity Recognition method based on multi-feature fusion and biaffine

Chinese Named Entity Recognition (CNER) focuses on precisely identifying predefined structural categories in unstructured Chinese text. Most existing CNER models do not consider the unique glyph and pinyin features of Chinese characters, but the rich semantic features hidden behind these features have a good effect on enhancing the judgment ability of language models. At the same time, it is difficult to identify the boundaries of Chinese nested entities, and accurately identifying the boundaries of entities within nested entities is also a difficult problem to solve. We propose a CNER method based on multi-feature fusion technology and biaffine mechanism to address the above issues: In the input representation layer, integrate the glyph and pinyin features of Chinese characters together, intuitively capturing the semantics of Chinese characters. Furthermore, biaffine mechanism has been introduced to provide a comprehensive view of the input on a global scale. This mechanism effectively converts the task of entity recognition into a problem of assigning scores to spans, hence enhancing the precision of identifying entity borders. In order to evaluate the efficacy of the model, a series of experiments were done on three Chinese entity recognition datasets: Resume, MSRA, and People Daily. The experimental results show that the solid boundary can be identified more accurately, and the F1 values of 96.49%, 96.26% and 96.19% are obtained respectively, which has a better recognition effect than the baseline model.

Nested Entity Recognition Method Based on Multidimensional Features and Fuzzy Localization

Nested named entity recognition (NNER) aims to identify potentially overlapping named entities. Sequence labeling method and span-based method are two commonly used methods in nested named entity recognition. However, the linear structure of sequence labeling method results in relatively poor performance, and span-based method requires traversing all spans, which brings very high time complexity. All of them fail to effectively leverage the positional dependencies between internal and external entities. In order to improve these issues, this paper proposed a nested entity recognition method based on Multidimensional Features and Fuzzy Localization (MFFL). Firstly, this method adopted the shared encoding that fused three features of characters, words, and parts of speech to obtain a multidimensional feature vector representation of the text and obtained rich semantic information in the text. Secondly, we proposed to use the fuzzy localization to assist the model in pinpointing the potential locations of entities. Finally, in the entity classification, it used a window to expand the sub-sequence and enumerate possible candidate entities and predicted the classification labels of these candidate entities. In order to alleviate the problem of error propagation and effectively learn the correlation between fuzzy localization and classification labels, we adopted multi-task learning strategy. This paper conducted several experiments on two public datasets. The experimental results showed that the proposed method achieves ideal results in both nested entity recognition and non-nested entity recognition tasks, and significantly reduced the time complexity of nested entity recognition.