Word Accuracy Research Articles

Русский музыкальный жаргон как предмет лингвистического изучения

e paper deals with Russian musical jargon treated as a professional sociolect of musicians. The material has been taken from the data presented in the Internet. Stylistically, musical jargon belongs to a colloquial register of the Russian language. A thematic classification of Russian musical jargon has been offered. This class of words and expressions may be subdivided into two unequal groups – specialized and non-specialized jargon units. The former comprises units reflecting the sphere of musical performance whereas the latter refer to various spheres of everyday life. Specialized musical jargon group is subdivided into three subgroups – nominations of subjects, objects and ways of musical performance. Nomination technique of the jargon described includes occasional affixal word formation, word composition, metaphorical transference, borrowings (mainly from the English language), dialectal and dated words, and onomatopoeia. Transference of musical notions into various sphere of reality is also a feature of the jargon described.

Improvement of post BER performance in computer communication on Viterbi algorithm

Computer communication is the pivotal exchange of information and data among computing devices, exerting substantial influence over network communication quality. The conventional approach has typically centred on enhancing Bit Error Rate (BER) performance to bolster information quality during the transmission process. However, our research pioneers a novel strategy, focusing on post-BER improvement. Our innovative method prioritizes enhancing information quality at the receiver's end by implementing a unique approach. We employ the Viterbi algorithm to elevate the quality of individual words, ensuring accurate word recognition and precise comprehension. The ultimate goal is to provide end-users with an unambiguous and accurate understanding of transmitted words. Our simulation results conclusively demonstrate the effectiveness of this approach. Following the reception of words, we successfully attain the correct word information with remarkable accuracy. This approach transforms word-level information processing, safeguarding against inaccuracies and ambiguities that may otherwise compromise the integrity of data transfer. In summary, our research introduces a paradigm shift in optimizing information quality within computer communication networks. By focusing on post-BER improvements and harnessing the power of the Viterbi algorithm, we enable the receiver to decode words with precision and clarity. This breakthrough promises to enhance the overall quality and reliability of information exchange, further solidifying the foundation of modern computer communication.

Terms for the Purple Color in the Bashkir Language

Purpose. The purpose of the article is to study the names for the purple color in the Bashkir language, which function in several versions. The study is relevant due to the lack of research on the names of the color, not only in the Bashkir, but also in the Turkic languages in general. The study is carried out using descriptive, comparative-historical methods, and the method of lexicosemantic analysis. The electronic corpora of the Bashkir language, the dialectological base, and lexicographic works acted as linguistic material on the Bashkir language.Results. It was revealed that four types of names are used to designate purple in the Bashkir language: 1) a borrowing from Persian; 2) analytical names, consisting of a combination of two color designations; 3) a loan translation from the Russian language; 4) a dialect word. It has been established that these names differ from each other in terms of their scope: 1) the name šämäxä, borrowed from the Persian language, is used in the modern literary language; 2) the word körän, which means ‘brown’ in the literary language, is used in dialects; 3) a loan translation from the Russian language – miläwšä töθ (‘violets color’) – is used in publicism; 4) analytical names are presented in the colloquial language: qïðïl kük ‘red blue’, qïðɣïlt kük ‘lit. reddish-blue’, körän kük ‘brown-blue’, zäŋgär kük ‘bluish blue’.

Language, Dialects and Climate Change

Human beings live in a relationship with each other in human society and solve their problems through interaction. After years and centuries, due to climate change, their language and dialect change and words are added according to their new surroundings. Today, due to the advent of the scientific and technical revolution, climatic conditions will continuously increase and change. Climate change makes it evident that dialects have not persisted and that they alter when agricultural conditions in rural areas change, resulting in the usage of various words there. What was not used before, for the peviously Qalba in field the terms Qalba, Sarbani, Motani, and Ghazj were used but now the tractor is used instead. If a new technique is used in the field instead of the old manual tools, the economic structure will also change, and the relations among the residents will change until a new color and a new type appear in the construction and other parts of the environment, houses, and new sides as well. With it in a new way and a new color in a new language. In dialect, new terminology is created instead of the old terminology. Climate change over the years and centuries has caused changes in language and dialect.

Research advanced in Chinese word segmentation methods and challenges

Chinese word segmentation refers to the process of dividing a sequence of Chinese characters into individual words. It constitutes a fundamental component of Chinese natural language processing. Due to the intricacies of the Chinese language, Chinese word segmentation has garnered significant attention from researchers. Based on a review of historical literature, segmentation methods can be broadly categorized into rule-based, statistical, semantic-based, and comprehension-based approaches. With the advancement of machine learning, neural networks have emerged as the mainstream algorithm for word segmentation. However, Chinese presents several unique challenges, leading to segmentation results that are less effective compared to morphological analysis in languages like English. Moreover, word segmentation faces new challenges such as dependency on the quality and scale of corpora, as well as domain-specific segmentation in diverse fields. Addressing these emerging challenges will undoubtedly become a focal point in future research endeavors in this field. This review provides a comprehensive summary of existing methods, discusses the current state of Chinese word segmentation, and outlines directions for addressing the evolving complexities in the field. As Chinese language processing continues to advance, finding robust solutions for accurate word segmentation remains a critical area of research.

Swift Prosodic Modulation of Lexical Access: Brain Potentials From Three North Germanic Language Varieties.

According to most models of spoken word recognition, listeners probabilistically activate a set of lexical candidates, which is incrementally updated as the speech signal unfolds. Speech carries segmental (speech sound) as well as suprasegmental (prosodic) information. The role of the latter in spoken word recognition is less clear. We investigated how suprasegments (tone and voice quality) in three North Germanic language varieties affected lexical access by scrutinizing temporally fine-grained neurophysiological effects of lexical uncertainty and information gain. Three event-related potential (ERP) studies were reanalyzed. In all varieties investigated, suprasegments are associated with specific word endings. Swedish has two lexical "word accents" realized as pitch falls with different timings across dialects. In Danish, the distinction is in voice quality. We combined pronunciation lexica and frequency lists to calculate estimates of lexical uncertainty about an unfolding word and information gain upon hearing a suprasegmental cue and the segment upon which it manifests. We used single-trial mixed-effects regression models run every 4 ms. Only lexical uncertainty showed solid results: a frontal effect at 150-400 ms after suprasegmental cue onset and a later posterior effect after 200 ms. While a model including only segmental information mostly performed better, it was outperformed by the suprasegmental model at 200-330 ms at frontal sites. The study points to suprasegmental cues contributing to lexical access over and beyond segments after around 200 ms in the North Germanic varieties investigated. Furthermore, the findings indicate that a previously reported "pre-activation negativity" predominantly reflects forward-looking processing. https://doi.org/10.23641/asha.25016486.

The Evaluation of Augmented Reality Dictionary to Improve English Vocabulary

English is an important subject for scoring in primary and secondary school exams in Malaysia. However, students are exposed to countless grammatical errors and are affected by the dialect they use in daily communication, especially for students who live in Kelantan. They are used to the use of dialect words in daily conversation. Therefore, they faced difficulties in understanding and translating the words from Bahasa Melayu to English. Furthermore, there is a lack of mobile application dictionaries to help primary school students to translate Kelantan’s dialect into English. The objectives of this research are (i) to analyse the needs and requirements to develop an AR mobile application to translate Kelantan’s dialect into English, (ii) to develop an AR mobile application that can look up the meaning of Kelantan’s dialect in English accurately, (iii) to evaluate the usability of the mobile application in terms of compatibility, consistency, flexibility, and learnability, and (iv) to evaluate the effectiveness of the mobile application to improve the vocabulary of the users. ADDIE model has been chosen in this research which consists of five phases involved in completing this research which is: (i) analysis phase, (ii) design phase, (iii) development phase, (iv) implementation phase, and (v) evaluation phase. The main contribution of this research is an AR mobile application Kelantan Dictionary called MyBrainy Kelate for primary school to improve English vocabulary. MyBrainy Kelate will help primary school students who have difficulties translating Kelantan’s dialect words into English words. The usability testing proved that MyBrainy Kelate has met the requirements of usability elements. Meanwhile, the effectiveness testing proves that the application is an effective tool for the user to improve their knowledge of English vocabulary. For future works, the research can be improvised by taking into consideration the game-based learning in the mobile application. The playability test can be conducted to test the playability of the game.

JOURNEY IN HERITAGE LANGUAGE PRESERVATION: VOICES OF THE KAGAN SPEAKERS

The emergence and embracement of immigrant languages threaten indigenous languages to be neglected resulting to become extinct. This qualitative study aimed to gather the oral literature and analyze the morphosemantic features of the Kagan language in Hagonoy, Davao del Sur to help preserve the language and the culture of the tribe which has gradually vanished because of assimilation with people of different culture and language. Kagan elders whose ages ranging from 46 to 92 years old were the informants, with nine assigned for the FGD and 14 for the IDI. Results revealed that Kagan’s preserved oral literature consists of prayer, lyric poetry, and poem. It also divulged that Kagan language is rich of distinct characteristics in terms of its morphology and semantics. The retrieved oral literatures’ morphological features include affixes, verb tenses, pronouns, descriptive adjectives, adverbs, and conjunctions. The semantic features are the deictic expressions, denotative words, connotative words, symbols, synonymy, antonymy, and dialectal words. Further, results of the study would provide essential information on the morphosemantic features which convey the existence of an ethnolinguistic group that bears its distinct cultural heritage. KEYWORDS: Education, Applied Linguistics, Kagan language, phenomenology, morphosemantic features, Philippines.

Exploring Visual Relationships via Transformer-based Graphs for Enhanced Image Captioning

Image captioning (IC), bringing vision to language, has drawn extensive attention. A crucial aspect of IC is the accurate depiction of visual relations among image objects. Visual relations encompass two primary facets: content relations and structural relations. Content relations, which comprise geometric positions content (i.e., distances and sizes) and semantic interactions content (i.e., actions and possessives), unveil the mutual correlations between objects. In contrast, structural relations pertain to the topological connectivity of object regions. Existing Transformer-based methods typically resort to geometric positions to enhance the visual relations, yet only using the shallow geometric content is unable to precisely cover actional content correlations and structural connection relations. In this article, we adopt a comprehensive perspective to examine the correlations between objects, incorporating both content relations (i.e., geometric and semantic relations) and structural relations, with the aim of generating plausible captions. To achieve this, first, we construct a geometric graph from bounding box features and a semantic graph from the scene graph parser to model the content relations. Innovatively, we construct a topology graph that amalgamates the sparsity characteristics of the geometric and semantic graphs, enabling the representation of image structural relations. Second, we propose a novel unified approach to enrich image relation representations by integrating semantic, geometric, and structural relations into self-attention. Finally, in the language decoding stage, we further leverage the semantic relation as prior knowledge to generate accurate words. Extensive experiments on MS-COCO dataset demonstrate the effectiveness of our model, with improvements of CIDEr from 128.6% to 136.6%. Codes have been released at https://github.com/CrossmodalGroup/ER-SAN/tree/main/VG-Cap .

An Intelligent Medical Q&A System Based on Natural Language Processing

Aim: To improve the accuracy of Chinese word splitting. Background: With the development of Internet technology, people want to get some effective medical information from the Internet, but there are still technical difficulties for non-specialists. At the same time, the level of medical construction can not keep up with the demand of patients for medical treatment, the phenomenon of doctor-patient conflicts has not been fundamentally solved, and the problem of difficult consultation prevails. With the arrival of the era of big data and artificial intelligence, medical Q&A has been applied. Objective: In order to meet the user's need to get the correct answer as soon as possible, medical Q&A needs to have high execution efficiency. The accuracy of Chinese participle directly affects the execution efficiency of Q&A. Improving the accuracy of Chinese participle can fundamentally improve the accuracy of medical Q&A and shorten the answering time. Methods: Improvement of the Chinese Segmentation Algorithm based on BI-LSTM-CRF using natural language processing technology. Based on the same medical Q&A dataset, the medical Q&A is trained and tested under three commonly used segmentation algorithms and the segmentation algorithm designed in this paper. Results: The experiments show that the Chinese Segmentation Algorithm studied in this paper improves the accuracy of medical Q&A and can improve the execution efficiency of medical Q&A. Conclusion: Based on the calculation and matching process of the same similar answers, different word-splitting methods directly affect the effect of medical Q&A in the later stage. The better the effect of segmentation, the higher the accuracy of the correct answers in medical Q&A. The improved LSTM-CRF split word accuracy designed in this paper achieves a good split word effect in the training process. Compared with the HMM segmentation algorithm, which has the best segmentation performance among the other three algorithms, the segmentation accuracy is improved, and the accuracy of the Q&A that delivers the correct answers is relatively high. Despite the improved accuracy in segmenting the medical dataset, the time complexity did not decrease much. The LSTMCRF combined network segmentation algorithm designed in this paper performs better in medical Q&A compared to other commonly used segmentation algorithms in terms of subject operating characteristics and larger regions surrounded by coordinate axes.

MCICT: Graph convolutional network-based end-to-end model for multi-label classification of imbalanced clinical text

The rapid growth of clinical text data requires accurate and powerful automated classification methods to support medical decision making and personalized healthcare. The multi-label classification task for clinical texts is designed to assign the most relevant set of labels to each clinical text. However, this task presents two significant challenges: (1) how to accurately extract fine-grained semantic features from complex clinical texts, and (2) how to effectively mitigate the issue of label class imbalance. To overcome these problems, we innovatively propose a novel Multi-label Classification of Imbalanced Clinical Text (MCICIT) model. In order to obtain fine-grained semantic features from clinical texts, we utilize the specialized pre-trained language model BioBERT, tailored for biomedical texts. To tackle the challenge of label class imbalance, we present a Co-occurrence Based and Embeddings with Additional Information Enhanced Graph Convolutional Network (CoEAI-GCN) module. On one hand, we enrich the label content by incorporating additional information to acquire more accurate word embeddings as the feature matrix. On the other hand, we combine the co-occurrence relationship of labels to construct a correlation matrix. Ultimately, label representations are learned through a graph convolutional network. By conducting multi-label classification experiments on two clinical text datasets extracted from real medical systems, our model achieves a 3.2% and 0.5% improvement in F1 scores, respectively, compared to state-of-the-art deep learning models. Additionally, we conduct ablation studies to explore the behaviors of the proposed model. These results together demonstrate that our proposed MCICT effectively enhances the classification performance of imbalanced clinical texts.

The problem of reflecting the variability of a word in a dialect dictionary (on the material of the “Pskov Regional Dictionary with Historical Data”)

The article is devoted to the problem of reflecting the variability of the Russian dialect word in dialect dictionaries published in Russia from the 19th to the 21st centuries. The study aims to consider the method of presenting different types of variants of a dialect word in various dialect dictionaries, with the main attention being paid to the analysis of works of dialect lexicography created since the middle of the 20th century until now. The submission of variants in the “Pskov Regional Dictionary”, which has been worked on at Leningrad State University — St. Petersburg State University (and Pskov Pedagogical Institute — Pskov State University) since the 1950s, is analyzed in most detail. Research objectives: to study, based on the relevant sections of the introductions and prefaces to dialect dictionaries of the Russian language, what exactly the authors of dictionaries consider to be variants of a dialect word, what types of variants they distinguish and how they are presented in their dictionaries. The novelty of the work lies in the consideration of the problem under study on the basis of a wide range of dialect dictionaries published in different regions of Russia and differently interpreting the theory of variance and describing lexical variation. The material of the study is both the theoretical introductory sections of Russian dialect dictionaries — namely, those parts that are devoted to the variability of the dialect lexeme — and the dialect dictionaries themselves, which differently present certain types of word variants in the dictionary and in a separate dictionary entry.

ДИАЛЕКТ В НЕМЕЦКОМ АНЕКДОТЕ

t. The aim of the research is the analysis of language variability in German jokes which contain dialect words. Every dialect has own phonetic features (which are rendered by graphical means in a written text), specific vocabulary and grammar. On the whole German dialects are highly researched. Against this background, works dwelling on the role of dialect in jokes are really very scarce. The research is based on written sources. The scientific novelty of the research lies in the description of the role of some means of dialect coloring in creating a comic effect in jokes about Berliners and Bavarians. The results obtained show that dialectcolored language means of different levels can be used separately or combine with one another. A representative of any dialect area can be described in a comic light. At the same time, any joke should always be viewed in a cultural context, without understanding which it is impossible to fully perceive it. In subsequent studies, attention can be focused on the dialect features of other regions of Germany.

Development of a neural network­based transcription method

In today's ever-evolving digital era, the utilization of audio and video materials has become increasingly prevalent in various spheres, including educational sectors, research initiatives, technological developments, and daily communication. This trend is driven by the convenience and efficiency of these mediums, offering quick access to information without the distractions often associated with more traditional methods like manual writing or typing, especially in scenarios where such activities are impractical, such as when traveling in a vehicle. However, this shift towards audio-visual content has introduced the challenge of converting these dynamic formats into text for a range of purposes. This need arises for several reasons. Firstly, editing and refining spoken content is often easier when it's transformed into a written format, allowing for a more thorough review and adjustment process. Secondly, in situations where individuals prefer or require written documentation — for instance, in academic or professional settings — transcription becomes a critical tool. Additionally, the clarity of audio and video content can vary greatly, with factors like diction, accent, background noise, and recording quality affecting the comprehensibility of the material. To address these challenges, transcription — the process of translating audio and video content into text — has become a valuable solution. It involves a meticulous process of listening, interpreting, and typing out the content, ensuring that the essence and nuances of the original material are accurately captured. This task demands not only attention to detail but also a deep understanding of the context and subject matter to ensure precision and reliability. In the realm of technology, advancements such as neural networks have revolutionized the transcription process. Neural networks, complex algorithmic structures that emulate human brain functioning, can learn, adapt, and process diverse types of data. In transcription, they are employed to recognize and interpret various speech patterns, accents, and languages, significantly enhancing the accuracy and speed of converting spoken words into written text. This integration of advanced technology in transcription not only streamlines the process but also opens new possibilities for accessibility, research, and data analysis, making it an indispensable tool in the modern world.

ПОЗАТЕЗАУРУСНА ЛЕКСИКА У ТВОРЧОСТІ Н. КАЗАНДЗАКІСА ТА О. ЕЛІТИСА

Background. The article focuses on the analysis of certain lexical units repeated in the texts of N. Kazantzakis and O. Elytis, which, despite their apparently neological character and absence in major Modern Greek dictionaries, cannot be definitively attributed to the individual author's neology. Neo-Hellenic studies experience a dearth of dictionaries documenting the specific language of major Modern Greek authors and their writings — a lacuna still in need of careful research into vocabularies not yet found in major dictionaries. The tendency of both authors to create new words on the basis of or on the models of archaic (mainly O. Elytis) and dialectal (mostly N. Kazantzakis) words, the misclassification of certain lexemes as authorial neology, the impossibility of lexicographically cover all layers of the Greek lexicon and the versatility of such a concept as "undictionaried words" (out-of-thesaurus, non-dictionary words, words not codified by dictionaries) in Greek scientific literature necessitate further study of the issue. The aim of this research is to focus on the analysis of "non-dictionary words" in the texts of the collections of both authors, to define their poetic function and different shades of meanings in the textual environment. Methods. In the presented analysis descriptive and special research methods were applied: methods of contextual, comparative and structural-semantic analysis, techniques of component and intertextual analysis. The material was selected by the method of continuous sampling; among the lexical units' selection criteria were: stylistic coloring, complete or partial absence of particular lexemes and their components in the major (or newest) dictionaries of Modern Greek language. All the selected lexical material is illustrated by examples from the original texts. Results. The present analysis revealed that some lexical units, which, at first glance, had all the features of the author's neology, on further investigation turned out to be documented earlier - they are present in other texts, articles, collections of literary pieces written by different authors, archives and dictionaries of different periods of Greek language development. Conclusions. As a unique linguistic material, the poems of O. Elytis and N. Kazantzakis have repeatedly become the subject of scientific research, both in terms of direct literary allusions and mythopoetic, but despite the large number of notable works, the scientific interest of researchers has not yet shifted more systematically to the compilation of specialized dictionaries. This issue is particularly acute with regard to the inexhaustible amount of still unexplored linguistic material collected by N. Kazantzakis for his poem 'The Odyssey'.

Dyslexia Analysis and Diagnosis Based on Eye Movement.

Dyslexia is a complex reading disorder characterized by difficulties in accurate or fluent word recognition, poor spelling, and decoding abilities. These challenges are not due to intellectual, visual, or auditory deficits. The diagnosis of dyslexia is further complicated by symptom variability, influenced by cultural and personal factors. This study leverages Virtual Reality (VR) advancements, eye movement tracking, and machine learning to create a virtual reading environment that captures eye movement data. This data extracts features such as eye movement metrics, word vectors, and saliency maps. We introduce a novel fusion model that integrates various machine learning algorithms to objectively and automatically assess dyslexia using physiological data derived from user interactions. Our findings suggest that this model significantly enhances the accuracy and efficiency of dyslexia diagnosis, marking an important advancement in educational technology and providing robust support for individuals with dyslexia. Although the sample size was limited to 10 dyslexic and 4 control participants, the results offer valuable insights and lay the groundwork for future studies with larger cohorts.

《阿Q正传》中方言词的对比分析研究：语义场的认知语言学视角

《阿Q正传》中方言词的对比分析研究：语义场的认知语言学视角

Иллюстративная зона регионального словаря: структура и содержательный потенциал интенционально фрагментированных воспоминаний носителей говоров

The article covers the various-aspect informative value of illustrative recollections in a regional dictionary. Its aim is to identify structural features of the illustrative area of the regional dictionary and its potential of presenting various types of information about antecedency. An Essay on the Dictionary of Kuban Region Sub-Dialects is taken as a source for examination, with a significant part of justifying examples therefor sampled from materials recorded by the authors of the article during multiyear dialectological expeditions. Pursuant to the purposes of lexicography, dialect macrotexts were subject to fragmentation, i.e. extraction of fragments of various size and structure in their shortened form. The article provides a graduated scale of intentional fragmentation by way of four structural types of intentionally fragmented illustrative recollections: illustrative recollections with actualized composition; illustrative recollections with reduced composition; illustrative recollections with zero composition; one-word remarks, equal to a dialect word, in an illustrative dialogue. The article demonstrates the informative potential of the identified structural types of illustrative recollections and its determinancy by volume. It defines the priority of illustrative recollections with actualized composition for communicating factual content. It also demonstrates that the informative value of illustrative recollections with reduced composition, as well as with the zero one, is ensured by presuppositions which exist in the subtext and must be present in the linguistic consciousness of the reader. The article notes the informative single-aspect feature of one-word remarks, equal to a dialect word, as part of an illustrative dialogue. The analysis of conceptual content, incorporated into illustrative recollections about singing and attitude towards singing in the past, proves the persistence of traditional cultural values of the Kuban Cossacks in the consciousness of Kuban region residents. The article concludes that the actualization of evaluative emotional content and conceptual content allows for illustrative recollections to be viewed as axiological texts by virtue of ethical and esthetical impact on the dictionary reader. In conclusion, the authors establish that the illustrative area of An Essay on the Dictionary of Kuban Region Sub-Dialects, due to broad presence of oral recollections therein, makes it a reliable source for their various-aspect study, due to the fact that the dictionary reconstructs the world of the past and reflects ethnical beliefs of the Kuban region dialect community. The findings allow for the argument that the regional dictionary, with its position at the intersection of two directions of communicational impact, i.e. between the collector and the informant, between the dictionary addressee and the lexicographer, is able to provide a mediated communicative contact between the dialect speaker and the reader, making the voice of the narrator heard through space and time. The authors declare no conflicts of interests.

The Restricted Omission (restriction of a subject to its verb, object, or both) is a Semantic Grammatical Study

This study discusses the omission of the subject and the object in a sentence from a new perspective, which tells that omission represents a form of restrictions; in other accurate words, the omitted form–that is not conceived–shall be deemed restricted, as its omission may mean that the verb shall not be restricted to it. From this perspective, the research tackles the passive verb forms and omission of a restricted object. ‏The research aims to semantically explain omission by proving the point which says that omission may occur to restrict the verb to the omitted form. To fulfill that aim, the research has adopted a descriptive and analytical approach and exceeded this, reaching the application to some positions of omission in the Holy Quran to explain in accordance with this point of view. Organizationally, the research consists of three chapters: the first one tackles the restriction of a verb to the subject, the second one tackles the restriction of a verb to the object, and the third chapter tackles the restriction of a verb to both the subject and object, then there was the conclusion that involves findings and recommendations.

Handwritten Vedic Sanskrit Text Recognition Using Deep Learning and Convolutional Neural Networks

Recognizing Vedic Sanskrit text is essential for accessing classical Indo-Aryan language, predominantly utilized in the Vedas. Currently, there is limited awareness about the Vedas, making this field a highly demanding and challenging area in pattern recognition. To accelerate progress in optical character recognition (OCR), deep learning methods are indispensable. This article presents a novel approach to Vedic Sanskrit text recognition, incorporating deep convolutional architectures with their respective interpretations. We introduce three modified 4-fold CNN architectures and the AlexNet model. Our system comprises a handwritten dataset containing 140 distinct Vedic Sanskrit words, with approximately 500 images per word, totaling around 70,000 images. The dataset is partitioned for training and testing in an 80:20 ratio. Training is conducted using 20% of the samples, and the resulting model is applied to the deep convolutional network with varied sets of neurons in their hidden layers. Our proposed method demonstrates robust support for accurate Vedic Sanskrit word classification. The recognition rate achieved in our research is 97.42%, with an average recognition time of 0.3640 milliseconds, surpassing existing CNN-based approaches.