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Готові списки джерел за темами / Rocessing handwriting data effectively / Статті в журналах

Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Rocessing handwriting data effectively.

Статті в журналах з теми "Rocessing handwriting data effectively"

Автор: Grafiati

Опубліковано: 7 червня 2025

Оновлено: 2 серпня 2025

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1

Gaurav, Pratap Singh Chauhun* Suman Rani. "An Overview of the 3-IN-1 TEXT TOOLS And Its Application." International Journal of Scientific Research and Technology 2, no. 5 (2025): 195–207. https://doi.org/10.5281/zenodo.15363797.

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Handwriting apps have revolutionized how users interact with devices, enabling the capture and recognition of handwritten input. This project focuses on designing and developinga Handwriting App using React JS for both the frontendand backend, leveragingits versatilityand performance to create a seamless user experience.  Existing solution softenrely on either platform-specific applicationsorthird-partyservices,whichcan be limited in functionality and costly. Many of these fail to provide an integrated environment for capturing, storing, and processing handwriting data effectively. Our pr

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2

Ren, Qing-Dao-Er-Ji, Lele Wang, Zerui Ma, and Saheya Barintag. "Offline Mongolian Handwriting Recognition Based on Data Augmentation and Improved ECA-Net." Electronics 13, no. 5 (2024): 835. http://dx.doi.org/10.3390/electronics13050835.

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Writing is an important carrier of cultural inheritance, and the digitization of handwritten texts is an effective means to protect national culture. Compared to Chinese and English handwriting recognition, the research on Mongolian handwriting recognition started relatively late and achieved few results due to the characteristics of the script itself and the lack of corpus. First, according to the characteristics of Mongolian handwritten characters, the random erasing data augmentation algorithm was modified, and a dual data augmentation (DDA) algorithm was proposed by combining the improved

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3

Rogushina, J. V., and A. Ya Gladun. "Application of ontological analysis for metadata processing in the interpretation of BIG DATA at the semantic level." PROBLEMS IN PROGRAMMING, no. 4 (December 2020): 055–70. http://dx.doi.org/10.15407/pp2020.04.055.

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The paper considers the main aspects of modern technologies applied for knowledge analysis to obtain information from Big Data. The analysis of the current state of research in this area shows that background knowledge subject areas of user interest represented by domain ontologies can be used both in order to effectively analysis of information acquried from certain sets of Big Data, and to make this acquisition more useful. With the help of such ontologies, users can formally describe the scope of their information needs, define the structure of the required information objects and explicitl

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4

Zhao, Zhenshen, and Tiancai Zhou. "High-Performance Roaming Display Algorithm for Ultra-Large Screen Handwriting." Journal of Computing and Electronic Information Management 14, no. 2 (2024): 1–3. https://doi.org/10.54097/m0df9s31.

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With the rapid development of smart education, the application of smart chalkboard software in teaching is becoming increasingly widespread. This article proposes a high-performance roaming display algorithm aimed at improving the smoothness and response speed of handwriting input on large screens. This algorithm achieves efficient handwriting input and real-time display through techniques such as data collection and preprocessing, dynamic partitioning and caching mechanisms, parallel computing and multi-threaded processing, as well as intelligent prediction and optimization. The experimental

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5

Chang, Chia-Hsiu, Yuan-Hsiung Tsai, and Hung-Cheng Tai. "Development and evaluation of an EMP course to teach fMRI technology and brain science in handwriting for university nursing students." Research and Practice in Technology Enhanced Learning 20 (September 25, 2024): 029. http://dx.doi.org/10.58459/rptel.2025.20029.

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In traditional nursing education, brain science is often limited to physiology, anatomy, and pathology, with little emphasis on advanced diagnostic tools like fMRI or the brain’s functional processes, such as language learning and handwriting. This study developed an English for Medical Purposes (EMP) course to teach university nursing students about fMRI technology and brain science related to handwriting, highlighting the importance of interprofessional education. The study aimed to (a) assess the course’s effectiveness in enhancing students’ knowledge in fMRI, brain science, and handwriting

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6

Pratiwi, Dian, Syaifudin Syaifudin, Ahmad Fauzy, and Mohammad Khasan. "Personality Type Analysis through Handwriting Characteristics Mapping using Invariant Moment Descriptors." Register 9, no. 2 (2023): 103–11. http://dx.doi.org/10.26594/register.v9i2.3420.

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Handwriting patterns are unique to each individual and can offer valuable insights into their mental health conditions, personality traits, behavioral tendencies, mindsets, and more. To effectively analyze someone's personality or solve a problem using their handwriting, it is crucial to employ suitable descriptors that accurately represent the essential information it contains. Therefore, this study aims to explore the application of invariant moments as descriptors to map personality types using the psychological technique of enneagrams in conjunction with handwriting patterns. The main proc

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7

Wang, S. H., S. Q. Lyu, M. L. Hou, Z. H. Gao, and M. Huang. "SURFACE HANDWRITING ENHANCEMENT OF ARTIFACTS BASED ON MANIFOLD LEARNING AND MIXED PIXEL DECOMPOSITION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 917–22. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-917-2022.

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Abstract. Written information on the surface of cultural relics can record important historical events. Due to the influence of natural and human factors, the surface of cultural relics fades and the words are difficult to identify. Take advantage of the hyperspectral data image and spectral unity and wide spectral range, a cultural relics surface handwriting enhancement method based on manifold learning and mixed pixel decomposition was proposed. First, the minimum noise fraction (MNF) transformation was carried out on the hyperspectral image, and then the top 10 bands were selected for inver

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8

Effendi, Edi Arif, Favorisen Rosyking Lumbanraja, Akmal Junaidi, and Admi Syarif. "Implementasi Metode Deep Learning Untuk Klasifikasi Gambar Tulisan Tangan." Jurnal Pepadun 4, no. 2 (2023): 100–106. http://dx.doi.org/10.23960/pepadun.v4i2.166.

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The advancement of current technology has led to the widespread utilization of pattern recognition in diverse fields, such as identifying signature patterns, fingerprints, faces, and handwriting. Human handwriting exhibits variations from one person to another, often making it challenging to read or recognize, which can hinder daily activities, particularly in transactions requiring handwritten input. Handwriting, being a distinct expression of individuals, can be effectively distinguished or recognized using pattern recognition methods, particularly through computer-based classification techn

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9

Singh, Puja. "General Characteristics of Handwriting and its Psychological Importance." Cognizance Journal of Multidisciplinary Studies 2, no. 2 (2022): 1–4. http://dx.doi.org/10.47760/cognizance.2022.v02i02.001.

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Each educated human have their own novel penmanship qualities which is normally inserted. Penmanship is the photo of the inner struggles continuing. The review shows that the penmanship appraisals can fill in as a viable indicator for the guess of the previously mentioned aggravations and perhaps some others too. The proficient finding of the aggravations in the area of brain science is mostly founded on indications referenced in books of analytic rule that are generally physical and mental yet semantic capacities like penmanship can likewise be utilized for something similar. It very well may

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10

Puja, Singh. "General Characteristics of Handwriting and its Psychological Importance." Cognizance Journal of Multidisciplinary Studies 2, no. 2 (2022): 1–4. https://doi.org/10.47760/cognizance.2022.v02i02.001.

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Each educated human have their own novel penmanship qualities which is normally inserted. Penmanship is the photo of the inner struggles continuing. The review shows that the penmanship appraisals can fill in as a viable indicator for the guess of the previously mentioned aggravations and perhaps some others too. The proficient finding of the aggravations in the area of brain science is mostly founded on indications referenced in books of analytic rule that are generally physical and mental yet semantic capacities like penmanship can likewise be utilized for something similar. It very well may

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11

Guo, Hang, Ji Wan, Haobin Wang, et al. "Self-Powered Intelligent Human-Machine Interaction for Handwriting Recognition." Research 2021 (April 1, 2021): 1–9. http://dx.doi.org/10.34133/2021/4689869.

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Handwritten signatures widely exist in our daily lives. The main challenge of signal recognition on handwriting is in the development of approaches to obtain information effectively. External mechanical signals can be easily detected by triboelectric nanogenerators which can provide immediate opportunities for building new types of active sensors capable of recording handwritten signals. In this work, we report an intelligent human-machine interaction interface based on a triboelectric nanogenerator. Using the horizontal-vertical symmetrical electrode array, the handwritten triboelectric signa

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12

Mas Diyasa, I. Gede Susrama, Pandu Ali Wijaya, and Yisti Vita Via. "Balinese Script Handwriting Recognition Using CNN and ELM Hybrid Algorithms." Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 14, no. 1 (2025): 49–59. https://doi.org/10.23887/janapati.v14i1.87524.

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One of the foundational scripts used in Balinese culture is the Balinese script, known as “Aksara Bali”. In its writing, Aksara Bali follows specific rules regarding distinctive stroke shapes that must be carefully maintained to preserve authenticity and readability. This study proposes the use of a hybrid algorithm combining Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) to recognize handwritten Balinese script characters. The preprocessing stage includes dataset splitting, rescaling, data augmentation, batch size adjustment, and visualization of class distribution. The

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13

Franz, Karly S., Grace Reszetnik, and Tom Chau. "On the Need for Accurate Brushstroke Segmentation of Tablet-Acquired Kinematic and Pressure Data: The Case of Unconstrained Tracing." Algorithms 17, no. 3 (2024): 128. http://dx.doi.org/10.3390/a17030128.

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Brushstroke segmentation algorithms are critical in computer-based analysis of fine motor control via handwriting, drawing, or tracing tasks. Current segmentation approaches typically rely only on one type of feature, either spatial, temporal, kinematic, or pressure. We introduce a segmentation algorithm that leverages both spatiotemporal and pressure features to accurately identify brushstrokes during a tracing task. The algorithm was tested on both a clinical and validation dataset. Using validation trials with incorrectly identified brushstrokes, we evaluated the impact of segmentation erro

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14

Feng, Chao, Zhongyuan Ji, and Jin Zhang. "Comparative Analysis of Dynamic Characteristics between Electronic Signature and Conventional Signature Based on Computer Vision Technology." Computational Intelligence and Neuroscience 2022 (June 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/4965908.

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During the last two or three decades where innovations in technology have been dominant, especially the rapid development of electronic information technology, various types of electronic devices have been developed for different application areas. It is this technological-assisted equipment that has drastic effects on the lifestyle of every creature in general and human beings in particular. In addition to the other activities or services, technology has enabled human beings to write on electronic devices, which is due to the fact that these devices will generate electronic signature handwrit

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15

Bazarbekov, I. М., M. T. Ipalakova, E. A. Daineko, and S. B. Mukhanov. "DEVELOPMENT AND DATA ANALYSIS OF A ROBO-PEN FOR ALZHEIMER’S DISEASE DIAGNOSIS: PRELIMINARY RESULTS." Herald of the Kazakh-British technical university 21, no. 3 (2024): 78–89. http://dx.doi.org/10.55452/1998-6688-2024-21-3-78-89.

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Alzheimer’s Disease (AD) poses a significant challenge in contemporary medicine, necessitating early and accurate diagnostic methods to manage its progression effectively. This study explores the development and application of the Robo-pen, an innovative diagnostic tool designed to detect early signs of cognitive decline through detailed handwriting analysis. The Robo-pen, equipped with an MPU-9250 sensor, captures three-dimensional coordinates, velocity, and acceleration of handwriting movements, crucial for assessing spatial control, movement consistency, speed variations, and the ability to

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16

Ting, Youyu, Wenan Tan, and Jinlong Lv. "Implementation and Optimization of Generative Adversarial Networks in Handwriting Image Modeling." Advances in Engineering Research Possibilities and Challenges 1, no. 3 (2025): 33. https://doi.org/10.63313/aerpc.2015.

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Generative Adversarial Networks (GAN), as an important research direction in the field of deep learning in recent years, have been widely used in many fields such as image and speech due to their excellent data generation ability. In this paper, an adversarial model for MNIST handwritten digital image generation is designed and implemented based on the classical GAN framework. The model adopts fully connected neural networks to construct the generator and dis-criminator and com-bines optimization techniques such as Label Smoothing and Batch Normalization to improve the training stability and i

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17

Lu, Jiaxin, Hengnian Qi, Xiaoping Wu, Chu Zhang, and Qizhe Tang. "Research on Authentic Signature Identification Method Integrating Dynamic and Static Features." Applied Sciences 12, no. 19 (2022): 9904. http://dx.doi.org/10.3390/app12199904.

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In many fields of social life, such as justice, finance, communication and so on, signatures are used for identity recognition. The increasingly convenient and extensive application of technology increases the opportunity for forged signatures. How to effectively identify a forged signature is still a challenge to be tackled by research. Offline static handwriting has a unique structure and strong interpretability, while online handwriting contains dynamic information, such as timing and pressure. Therefore, this paper proposes an authentic signature identification method, integrating dynamic

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18

Rabi, Mouhcine, and Mustapha Amrouche. "Enhancing Arabic Handwritten Recognition System-Based CNN-BLSTM Using Generative Adversarial Networks." European Journal of Artificial Intelligence and Machine Learning 3, no. 1 (2024): 10–17. http://dx.doi.org/10.24018/ejai.2024.3.1.36.

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Arabic Handwritten Recognition (AHR) presents unique challenges due to the complexity of Arabic script and the limited availability of training data. This paper proposes an approach that integrates generative adversarial networks (GANs) for data augmentation within a robust CNN-BLSTM architecture, aiming to significantly improve AHR performance. We employ a CNN-BLSTM network coupled with connectionist temporal classification (CTC) for accurate sequence modeling and recognition. To address data limitations, we incorporate a GANs based data augmentation module trained on the IFN-ENIT Arabic hand

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19

Ono, Keiko, Daisuke Tawara, Yuki Tani, Sohei Yamakawa, and Shoma Yakushijin. "U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution." Applied Sciences 13, no. 19 (2023): 10798. http://dx.doi.org/10.3390/app131910798.

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Bone semantic segmentation is essential for generating a bone simulation model for automatic diagnoses, and a convolution neural network model is often applied to semantic segmentation. However, ground-truth (GT) images, which are generated based on handwriting borderlines, are required to learn this model. It takes a great deal of time to generate accurate GTs from handwriting borderlines, which is the main reason why bone simulation has not been put to practical use for diagnosis. With the above in mind, we propose the U-net-based semi-automatic semantic segmentation method detailed in this

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20

Shahini, Sildi, Ardiana Topi, and Forsian Elezi. "The Application of Deep Learning in Optical Character Recognition." INGENIOUS 4, no. 2 (2024): 111–34. https://doi.org/10.58944/kbpa8944.

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Optical Character Recognition (OCR) is an essential technology for document digitization, enabling the conversion of scanned paper documents, PDFs and images into editable and searchable data. This paper focuses on the application of deep learning in OCR, particularly in digitizing handwritten medical prescriptions, where accuracy is critical for reducing errors and improving healthcare outcomes. Traditional OCR methods face challenges when dealing with handwritten texts due to the variability in handwriting styles and the quality of scanned documents. These limitations can result in recogniti

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21

Researcher. "INTEGRATED SYSTEM FOR FORENSIC DOCUMENT ANALYSIS: MULTISCRIPT RECOGNITION AND HANDWRITTEN SIGNATURE VERIFICATION." Journal of Computer Engineering and Technology (JCET) 7, no. 2 (2024): 1–16. https://doi.org/10.5281/zenodo.13737405.

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BACKGROUND: Forensic document analysis plays a crucial role in verifying the authenticity of signatures and recognizing handwritten content. Traditional methods often struggle with diverse handwriting styles and various script forms, necessitating the development of integrated systems that enhance the accuracy and efficiency of document examination. This study introduces a novel approach to forensic document analysis by integrating multiscript recognition and handwritten signature verification into a unified system.METHODS: The integrated system employs a quantitative experimental design utili

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22

A., Anisha B.E. M.E, Shelly. A. T. Femima, R. K. Benitta., and Selciya. T.L Amala. "Parkinson's Disease Detection using Spiral Drawings." International Journal of Innovative Science and Research Technology 8, no. 5 (2023): 2658–63. https://doi.org/10.5281/zenodo.8021545.

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Parkinson's disease is a neurological disorder that primarily affects people over the age of 60, often leading to motor impairment (MI) such as tremors, rigidity, and slowness. The disease's severity has been found to be linked to a decline in handwriting quality, with patients exhibiting reduced speed and pressure while writing. Biomarkers can aid in the diagnosis, monitoring, and prediction of the disease's progression, making it critical to accurately identify them. A convolutional neural network (CNN) is used in this study to analyze spiral drawing patterns from Parkinson's

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23

Soundes, Mekki, and Labdaoui Ahlam. "A systematic study of autoencoder hyperparameters for effective feature learning in image recognition tasks: insights from handwriting dataset." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e12308. https://doi.org/10.54021/seesv5n2-812.

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This research investigates the potential of autoencoders to enhance handwritten digit recognition using the MNIST dataset. Autoencoders, with their encoding and decoding mechanisms, effectively capture essential data patterns, making them powerful tools for feature extraction and dimensionality reduction. The study evaluates various autoencoder architectures, including shallow and deep designs, by fine-tuning hyperparameters such as epochs, batch size, and learning rate to optimize model representations and improve recognition performance. Performance is measured using metrics like Mean Square

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24

Boudjella, Aissa, Brahim Belhouari Samir, and Omar Kassem Khalil. "Handwritten Character Recognition Based on a Multiple Fermat's Spiral." Advanced Materials Research 774-776 (September 2013): 1629–35. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1629.

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This paper describes a new feature extraction method which can be used very effectively in combination with Cluster K-Nearest Neighbor (CKNN) and KNN Classifier for image recognition. We propose handwritten English character recognition using Fermat's spiral approach to convert an image space into a parameter space. The system is implemented and simulated in MATLAB, and its performance is tested on real alphabet handwriting image. Fifteen (15) alphabet classes were created to carry out the experiment. Each class contains 9 alphabets {a, b, c, d, e, f, g, h, i}. The total of 15x9=135 alphabet i

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25

Tribble, Curtis G. "Are You Making Yourself Clear? You Can’t Communicate, or Think, Effectively If You Can’t Write Clearly." Heart Surgery Forum 22, no. 3 (2019): E271—E276. http://dx.doi.org/10.1532/hsf.2609.

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n the not too distant past, illegible handwriting was considered to be the biggest problem with medical record keeping. Now the primary problem with medical records is that they are disorganized, and usually undigested, data dumps. A solution to at least part of this problem lies in utilizing the principles of the problem-oriented record. When one contemplates the optimal format for progress notes, it is worth considering the purposes of progress notes. While progress notes do, of course, play a role in billing, the primary purposes of a progress note should be to provide efficient and e

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26

Luchev, Detelin, Maksim Goynov, Desislava Paneva-Marinova, Radoslav Pavlov, and Konstantin Rangochev. "Repertoire of Medieval South Slavic Manuscripts and Scribes in Research Context." Chuzhdoezikovo Obuchenie-Foreign Language Teaching 51, no. 1 (2024): 75–89. http://dx.doi.org/10.53656/for2024-01-09.

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Research work, presented in this article, aims to create IT tools for the study and research of handwriting, transcription and writing in general. It provides tools for structuring, processing, managing, visualizing and analyzing this data, which greatly facilitates research and study processes. An online accessible digital repertorium (web-based software environment) has been developed, storing information about valuable manuscripts and copyists with significant contributions to the development of South Slavic languages, writing and culture. Each written resource is digitized and described ac

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27

Dubey, Parul, Manjushree Nayak, Hitesh Gehani, Ashwini Kukade, Vinay Keswani, and Pushkar Dubey. "Enhancing realism in handwritten text images with generative adversarial networks." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2370–79. https://doi.org/10.11591/eei.v14i3.9190.

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Image synthesis is particularly important for applications that want to create realistic handwritten documents, which is why handwritten text generation is a critical area within its domain. Even with today's highly advanced technology, generating diverse and accurate representations of human handwriting is still a tough problem because of the variability in style. In this study, we tackle the problem of instability during the training phase of generative adversarial networks (GANs) for generating handwritten text images. Using the MNIST dataset, which includes 60,000 training and 10,000 test

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28

Irfan, Irfan, Irwan Jaya, and Sadid Saneva. "DIGITALIZATION OF THE AERODROME CONTROL TOWER OPERATION LOGBOOK BASED ON THE WEBSITE." Journal of Airport Engineering Technology (JAET) 4, no. 2 (2024): 53–59. http://dx.doi.org/10.52989/jaet.v4i2.140.

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This research focused on digitalizing a web-based Operational Logbook system for the Aerodrome Control Tower used by Air Traffic Controllers (ATC) at the Biak Sub-branch. The current manual Operational Logbook system, which relies on physical books and handwriting, is inefficient due to the need for substantial storage space and the susceptibility to damage. This research aimed to address these issues by providing a web-based application that facilitates easier, faster, and more efficient data entry and retrieval. This research method employed Rapid Application Development (RAD), effectively d

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Tauseef, Muhammad, Syed Kazim Shah, Fatima Tul Zahra Sulehri, and Mehwish Kalsoom. "An Evaluation of Grade Six English Curriculum of Beaconhouse School System in Pakistan." International Journal of English Language Education 3, no. 2 (2015): 44. http://dx.doi.org/10.5296/ijele.v3i2.7824.

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<p>Curriculum and instruction material are considered fundamental tools in ELT throughout the world. Consideration of utmost significance of the curriculum requires its evaluation process for measuring its effectiveness in facilitating teaching/learning objectives. The present study is an attempt to evaluate grade six English curriculum of Beaconhouse School System. To the end, features based on the objectives of the curriculum were evaluated to determine whether learning of language, listening, reading, speaking, and writing, vocabulary and understanding of grammar, literature (both fic

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30

Liu, Yu, Li Shen, Xiang Han, and Cong Wang. "Fadable ink writing recognition based on laser-induced breakdown spectroscopy and machine learning." Laser Physics Letters 22, no. 5 (2025): 055205. https://doi.org/10.1088/1612-202x/adcde9.

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Abstract A method for restoring fadable ink writings using laser-induced breakdown spectroscopy (LIBS) combined with machine learning was proposed. This research employed dimensionality reduction and clustering analysis to process LIBS data, significantly improving analytical efficiency and accuracy. Unlike traditional chemical detection methods, this approach minimizes chemical damage to samples while ensuring operator safety. Compared to physical restoration techniques, it achieves higher accuracy and lower operational costs. Experimental results demonstrated stable recognition performance i

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Zhang, Mingxing, Hongpeng Li, Tian Ge, Zhaozong Meng, Nan Gao, and Zonghua Zhang. "Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network." Measurement Science Review 22, no. 4 (2022): 193–201. http://dx.doi.org/10.2478/msr-2022-0024.

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Abstract The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of weara

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Sukemi Kamto Sudibyo, Eni Endaryati, Vivi Kumalasari Subroto, Sri Wahyuning, Nur Rokhman, and Fitri Nur Romdhonah. "SISTEM INFORMASI AKUNTANSI KEUANGAN KANTOR DESA TAMBAKREJO KENDAL METODE CASH BASIS." Jurnal Akuntansi dan Bisnis 4, no. 2 (2024): 11–19. https://doi.org/10.51903/jiab.v4i2.798.

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The Tambakrejo Village office has problems in managing village finances in recording and presenting financial reports. Currently, the Tambakrejo village office uses a manual reporting system by handwriting it in a book. Data is stored in archives which may be lost or damaged, requires a long time to search if the data is to be used or viewed again, delays in preparing village financial reports due to ineffective recording and management of financial reports. The aim of this research is to produce a Financial Accounting Information System Using the Cash Basis Method in Tambakrejo Kendal Village

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Alali, Diseph, Ojekudo Nathaniel A, and Egbono Frank Fubara. "Automated-Information-Retrieval-System-for-Hospital-Records-Management." International Journal of Advance Research and Innovation 11, no. 3 (2023): 7–10. https://doi.org/10.69996/ijari.2023002.

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This Research work in its present form is a result of poor management of retrieval patient’s information in hospitals. As a result of that, papers are wasted to stock data of patients in the hospital, time consuming to search for patient information and also handwriting of some administrators are not very clear. The idea is to develop a java program application software for the management and ease retrieval of patients’ information in hospitals, and this led to the development of automated information retrieval system for hospital record management. It commences by first reviewing existing sys

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Wang, Qianglong, Xiaoguang Gao, Kaifang Wan, Fei Li, and Zijian Hu. "A Novel Restricted Boltzmann Machine Training Algorithm with Fast Gibbs Sampling Policy." Mathematical Problems in Engineering 2020 (March 20, 2020): 1–19. http://dx.doi.org/10.1155/2020/4206457.

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The restricted Boltzmann machine (RBM) is one of the widely used basic models in the field of deep learning. Although many indexes are available for evaluating the advantages of RBM training algorithms, the classification accuracy is the most convincing index that can most effectively reflect its advantages. RBM training algorithms are sampling algorithms essentially based on Gibbs sampling. Studies focused on algorithmic improvements have mainly faced challenges in improving the classification accuracy of the RBM training algorithms. To address the above problem, in this paper, we propose a f

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Vajda, Szilárd, Thomas Plötz, and Gernot A. Fink. "Camera-Based Whiteboard Reading for Understanding Mind Maps." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 03 (2015): 1553003. http://dx.doi.org/10.1142/s0218001415530031.

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Mind maps, i.e. the spatial organization of ideas and concepts around a central topic and the visualization of their relations, represent a very powerful and thus popular means to support creative thinking and problem solving processes. Typically created on traditional whiteboards, they represent an important technique for collaborative brainstorming sessions. We describe a camera-based system to analyze hand-drawn mind maps written on a whiteboard. The goal of the presented system is to produce digital representations of such mind maps, which would enable digital asset management, i.e. storag

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Chu, Jiawei, Xiu Kan, Yan Che, Wanqing Song, Kudreyko Aleksey, and Zhengyuan Dong. "Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy." Sensors 24, no. 17 (2024): 5624. http://dx.doi.org/10.3390/s24175624.

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Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot’s judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system’s complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese elect

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Pelatero, Leizel H. "Project AKAY Approach: A Reading Intervention for Non-Readers." Education Reform and Development 5, no. 1 (2023): 24–33. http://dx.doi.org/10.26689/erd.v5i1.5386.

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The Project Adequate Knowledge Acquisition for Young Learners (AKAY) approach serves as an intervention designed to support Grade 4 students who have experienced significant cognitive challenges due to the COVID-19 pandemic. This approach places a strong emphasis on the advantages of homogenous grouping in the teaching process. Its main objective is to assist students in acquiring fundamental skills, including knowledge of the alphabet, letter sounds, reading of consonant-vowel-consonant (CVC) pattern words, numerical comprehension, handwriting of names, and following simple directions. This s

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Nurhayati, Sri, Nur Sucahyo, and Selawati Selawati. "PENERAPAN METODE PIECES DALAM PENGEMBANGAN SISTEM E-COMMERCE PENJUALAN PRODUK KOMPUTER." JRIS: JURNAL REKAYASA INFORMASI SWADHARMA 1, no. 1 (2021): 34–39. http://dx.doi.org/10.56486/jris.vol1no1.63.

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E-commerce is a trading transaction activity through the internet network. By utilizing e-commerce, sellers can offer their products online to make it easier for them to shop, transact, and deliver effectively and efficiently. Using an e-commerce website as a sales tool, will expand the product marketing area and make it easier for buyers to select and order these products to increase the sales turnover of the store. Toko Blora is an electronics store located in Harco Mangga Dua. This shop sells several electronic products ranging from PCs, Laptops / Notebooks, parts, and accessories for PCs a

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Wahyudi, Dedy, Tries Handriman Jamain, Peris Hamdanur, and Ahmad Zuhrofi. "PELATIHAN APLIKASI SURAT MENYURAT DAN LAPORAN KEUANGAN KAS RT.05 RW.01 KELURAHAN CIPAYUNG JAYA KOTA DEPOK." Jurnal Pengabdian Bukit Pengharapan 4, no. 2 (2024): 24–30. https://doi.org/10.61696/jurdian.v4i2.386.

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In the area that is a partner for community service activities, which is located in the RT in the area RT.05, RW.01, Cipayung Jaya Village, Cipayung District, Depok City, the management is not good in serving administrative activities for the local community, namely in processing correspondence and other than The Partner does not understand about Cash Financial Reports where partners still carry out Cash Financial Reports manually, namely in the form of writing in ledgers, difficulties in searching for data, loss of data, reporting financial cash still using handwriting and file damage. The so

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Naik, Saurav, Prathamesh Pathare, Muzammil Qureshi, Chirag Kalaswad, Akshat Joshi, and Nayan Paliwal. "Recognizing Handwritten Digits on MNIST Dataset using KNN Algorithm." Journal of Artificial Intelligence and Imaging 1, no. 2 (2024): 10–18. http://dx.doi.org/10.48001/joaii.2024.1210-18.

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Handwritten digit recognition is a critical job in computer vision and is used as a frequent benchmark for testing machine learning algorithms. This work describes the creation of a recognition system utilizing the K-Nearest Neighbors (KNN) method, which was chosen for its simplicity and ease of understanding. The system is built on the MNIST dataset, which contains a vast number of photographs of handwritten digits. The process begins with data collection and investigation, which involves analyzing the content and properties of the MNIST dataset to better comprehend the range of handwriting s

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Gul, Kiran, Waheed Shahzad, Ali Raza, et al. "An investigation to identify the factors that cause failure in English essay, precis, and composition papers in CSS exams." Journal of Autonomous Intelligence 7, no. 5 (2024): 1254. http://dx.doi.org/10.32629/jai.v7i5.1254.

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<p>The research study aims to examine why candidates in Pakistan failed the English Essay, Precis, and Composition sections of the Central Superior Services (CSS) tests. Those candidates chosen for various civil service positions take the prestigious and difficult CSS exam. The study aims to discover candidates’ difficulties in these particular CSS exam sections and investigate methods for enhancing their English language ability. A mixed-methods strategy is used in the research process to collect both quantitative and qualitative data. Participants in the CSS exam who once took the Engl

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Zhang, Weiqiang, Linfeng Deng, Xiaozhou Lü, et al. "Advanced handwriting identification: Triboelectric sensor array integrating with deep learning toward high information security." InfoMat, June 4, 2025. https://doi.org/10.1002/inf2.70002.

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AbstractHandwriting identification is widely accepted as scientific evidence. However, its authenticity is questioned because it depends on the appraiser's professional skills and susceptibility to deliberate false identification by expert witnesses. Consequently, there is an urgent need for an effective handwriting identification system (HWIS) that reduces reliance on the appraiser's skills and mitigates the risk of international false identification. Here, we report a HWIS that integrates a self‐powered handwriting signal data acquisition device with an advanced deep learning architecture po

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Chernov, Yury. "Formalized Assessment of Handwriting Deterioration Caused by Dementia." Medinformatics, July 10, 2025. https://doi.org/10.47852/bonviewmedin52025601.

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Handwriting is a sensitive indicator of both cognitive and physical changes, making it a promising tool for the early detection of neurodegenerative conditions such as cognitive impairment (CI) and Alzheimer’s disease (AD). Because dementia-related handwriting changes vary between individuals, a systematic and quantitative approach is essential. This study employs a robust analytical framework incorporating 41 handwriting and 3 linguistic features, previously shown strong discriminatory power to effectively differentiate individuals with CI/AD from healthy controls. The current work focuses on

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Yan, Guihua, Xichen Hu, Ziyue Miao, et al. "Alphabet Handwriting Recognition: From Wood‐Framed Hydrogel Arrays Design to Machine Learning Decoding." Advanced Science, November 4, 2024. http://dx.doi.org/10.1002/advs.202404437.

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AbstractHandwriting recognition is a highly integrated system, demanding hardware to collect handwriting signals and software to deal with input data. Nonetheless, the design of such a system from scratch with sustainable materials and an easily accessible computing network presents significant challenges. In pursuit of this goal, a flexible, and electrically conductive wood‐derived hydrogel array is developed as a handwriting input panel, enabling recognizing alphabet handwriting assisted by machine learning technique. For this, lignin extraction‐refill, polypyrrole coating, and polyacrylic a

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Tang, Qizhe, Xiaoya Zhang, Chu Zhang, Qing Lang, Hengnian Qi, and Lina Wang. "Optimized sequential classification models for mild cognitive impairment screening based on handwriting and speech data." Journal of Alzheimer’s Disease, July 20, 2025. https://doi.org/10.1177/13872877251359874.

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Background Handwriting and speech are served as reliable signatures for detecting cognitive decline, playing a pivotal role in the early diagnosing Alzheimer's disease (AD) and mild cognitive impairment (MCI). However, current unimodal approaches for diagnosing AD and MCI have demonstrated constraints in classification accuracy, potentially overlooking the synergistic value of combining handwriting and speech data. Objective Presenting an innovative multi-modal screening classification model, that harnesses handwriting and speech analysis to enhance MCI detection, aiming to overcome the constr

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Xu, Guangxiang, Zebin Wang, Kedi Xu, et al. "Decoding Handwriting Trajectories from Intracortical Brain Signals for Brain‐to‐Text Communication." Advanced Science, July 28, 2025. https://doi.org/10.1002/advs.202505492.

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AbstractThe potential to decode handwriting trajectories from brain signals has yet to be fully explored in clinical brain‐computer interfaces (BCIs). Here, intracortical neural signals are recorded from a paralyzed individual during attempted handwriting of complex characters. An innovative decoding framework is introduced to address both shape and temporal distortions between neural activity and movement, effectively resolving the misalignment issue commonly encountered in clinical BCIs due to the lack of accurate movement labels. The results demonstrated the reconstruction of highly accurat

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Pan, Yuecai, Daoerji Fan, Huijuan Wu, and Da Teng. "A new dataset for mongolian online handwritten recognition." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-022-27267-8.

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AbstractThis paper introduces a new traditional Mongolian word-level online handwriting dataset, MOLHW. The dataset consists of handwritten Mongolian words, including 164,631 samples written by 200 writers and covering 40,605 Mongolian common words. These words were selected from a large Mongolian corpus. The coordinate points of words were collected by volunteers, who wrote the corresponding words on the dedicated application for their mobile phones. Latin transliteration of Mongolian was used to annotate the coordinates of each word. At the same time, the writer’s identification number and m

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Afzali, Parvaneh, Abdoreza Rezapour, and Ahmad Rezaee Jordehi. "Offline writer identification using deep feature concatenation." Journal of Intelligent & Fuzzy Systems, September 22, 2023, 1–13. http://dx.doi.org/10.3233/jifs-231889.

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Handwriting is an individual trait that serves as evidence to authenticate a particular writer. Identifying the writer of a handwritten text has shown encouraging results in examining historical and forensic documents. In this paper, we propose a novel offline writer identification system based on the challenging analysis of small amount of data to extract distinct patterns. In our deep network, the feature extraction process relies on a specially designed dual-path architecture, and the resulting embeddings are concatenated to produce the final learned features. To deal with a variety of unce

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Moinuddin, Siddiqui Mohammed Khaja, Suneet Kumar, Sayeed Ahmed, and Arvind Kumar Jain. "Study on handwriting features of ambidextrous persons by using image processing." International journal of health sciences, May 21, 2022, 7003–21. http://dx.doi.org/10.53730/ijhs.v6ns3.7636.

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Handwriting Features of Ambidextrous Persons have been classified as a multi-class classification issue in the deep learning framework, where image recognition task is effectively to test a classifier who can efficiently discriminate each line of paragraph of handwriting. This is very common for a single classifier to be performed on different data sets with a standard deep learning algorithm. It may also be that the same classifying system performs differently with highly differing image instances due to the various manuscript styles of various people on the same numbers. In order to address

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Tabatabaey–Mashadi, Narges, Rubita Sudirman, and Puspa Inayat Khalid. "An Evaluation of Children’s Structural Drawing Strategies." Jurnal Teknologi 61, no. 2 (2013). http://dx.doi.org/10.11113/jt.v61.1632.

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Inspecting children’s structural drawing is developmentally and psychologically important. Today’s digital availability of such data from electronic tablets, inspires automatic analysis; however, converting such data to an informative feature vector for further analysis and identification of related indicators, needs appropriate algorithms. This study presents simple, fast methods for detecting X, O and 4 basic lines’ drawing strategies. The functionality of the algorithms is tested on an available database subtending the performances of 74 (6–7years) pupils. Results demonstrate typical behavi

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