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Journal articles on the topic 'Handwriting-to-Text'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Subash, R., K. Prasannavenkatesan та Dr R. Sunitharam. "Digital Text to Users Handwriting (தமிழ்)". International Journal of Soft Computing and Engineering 13, № 1 (2023): 10–15. http://dx.doi.org/10.35940/ijsce.a3588.0313123.

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Converting digital text to handwriting is a simple process because of the abundance of software and websites that do it, like texttohandwriting.com. The Text to Handwriting Converter is a free artificial intelligence-based tool that translates computer text into handwritten text with ease. An individual's handwriting format is saved as an input, converted into text, and then shown as an output. Image processing techniques can be used to process the handwriting. It is possible to use the alphabets of specific languages, such as Tamil (தமிழ்), English, etc. The text of the input is finally displ

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Dr., R. Sunitharam, Subash R. та Prasannavenkatesan K. "Digital Text to Users Handwriting (தமிழ்)". International Journal of Soft Computing and Engineering (IJSCE) 13, № 1 (2023): 10–15. https://doi.org/10.35940/ijsce.A3588.0313123.

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<strong>Abstract: </strong>Converting digital text to handwriting is a simple process because of the abundance of software and websites that do it, like texttohandwriting.com. The Text to Handwriting Converter is a free artificial intelligence-based tool that translates computer text into handwritten text with ease. An individual's handwriting format is saved as an input, converted into text, and then shown as an output. Image processing techniques can be used to process the handwriting. It is possible to use the alphabets of specific languages, such as Tamil (தமிழ்), English, etc. The tex

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Arya, Anil, A. Ashiq, M. S. Aswathy, and P. S. Akhila. "A Comparative Review of Different Techniques for Handwriting to Text Conversion." Advanced Innovations in Computer Programming Languages 7, no. 1 (2024): 1–9. https://doi.org/10.5281/zenodo.13766826.

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<em>Handwriting to text conversion, also known as handwriting recognition, is the process of converting handwritten text into machine-readable text. This article presents a comparative review of the different machine learning techniques for handwriting to text conversion. It highlights the works of many researchers and provides an analysis of the various machine-learning techniques that are used for the handwriting to text conversion<strong>.</strong></em>

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Newalkar, Akshat, Himanshu Khade, Dhiraj Khandare, and Divy Patel. "CNN-Powered Handwriting to Digital Text Converter." International Journal of Ingenious Research, Invention and Development (IJIRID) 3, no. 5 (2024): 355–66. https://doi.org/10.5281/zenodo.14016761.

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The technology has become a crucial component in the digital transformation of documents for banks, educational institutions, and other sectors. In this paper, we design a handwritten character-to-text converter using CNN, where the input is a handwritten character and it is converted into computerized text. You see, CNNs are pretty darn good at image processing, and basically, what we're doing in the input debugger is detecting individual characters from a wide range of horrible handwritten gibberish. The model is trained on a dataset of handwritten characters, and its hierarchical feature ex

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Ke, Hong Chang, and De Gang Kong. "A New Text-Independent Handwriting Identification Method." Applied Mechanics and Materials 556-562 (May 2014): 4357–60. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.4357.

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For the disadvantage of traditional direction index histogram (DIH) handwriting identification method, an improved text-independent handwriting identification algorithm is presented. The handwriting image which is prepared to test needs pre-processing, then the normalized image can be obtained. Based on the features distance two factors can be extracted: the writing influence factor and the character influence factor. Compared the features of the handwriting image which is prepared to test with the features of the sample handwriting image, The better handwriting identification accuracy rate ca

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Shonenkov, A. V., D. K. Karachev, M. Y. Novopoltsev, M. S. Potanin, D. V. Dimitrov, and A. V. Chertok. "Handwritten text generation and strikethrough characters augmentation." Computer Optics 46, no. 3 (2022): 455–64. http://dx.doi.org/10.18287/2412-6179-co-1049.

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We introduce two data augmentation techniques, which, used with a Resnet-BiLSTM-CTC network, significantly reduce Word Error Rate and Character Error Rate beyond best-reported results on handwriting text recognition tasks. We apply a novel augmentation that simulates strikethrough text (HandWritten Blots) and a handwritten text generation method based on printed text (StackMix), which proved to be very effective in handwriting text recognition tasks. StackMix uses weakly-supervised framework to get character boundaries. Because these data augmentation techniques are independent of the network

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Chen, Rui, Bin Fang, and Patrick Shen-Pei Wang. "Chinese Handwriting Identification Method Based on Keyword Extraction." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 11 (2017): 1753004. http://dx.doi.org/10.1142/s0218001417530044.

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Text-independent handwriting identification methods require that features such as texture are extracted from lengthy document image; while text-dependent handwriting identification methods require that the contents of the documents being compared are identical. In order to overcome these confinements, this paper presents a novel Chinese handwriting identification technique. First, Chinese characters are segmented from handwriting document, then keywords are extracted based on matching and voting of local features of character. Then the same-content keywords are used to build training sets, and

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Leedham, C. G., and Y. Qiao. "High speed text input to computer using handwriting." Instructional Science 21, no. 1-3 (1992): 209–21. http://dx.doi.org/10.1007/bf00119666.

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Pramodita, Potta, P. Nancy Celine, and Gugulothu Krishnaveni. "Handwritten Text Recognition: A Deep Learning Based Approach to Digitize Handwritten Text." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem03136.

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Our project employs deep learning methods to digitize handwritten pages into text. The process has a number of steps, ranging from identifying individual words, and identifying individual characters. Through the use of convolutional neural networks (CNNs) and other sophisticated machine learning models, the project seeks to perform accurate and efficient recognition of handwritten text. The model is trained on the IAM Handwriting Dataset, which contains a diverse collection of handwritten text samples, allowing the system to generalize well across different handwriting styles. Key components o

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Shrawankar, Urmila. "Standardization of Handwritten Words to Improve Readability." International Journal of Technology Diffusion 10, no. 3 (2019): 1–17. http://dx.doi.org/10.4018/ijtd.2019070101.

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Everyone has different handwriting, and this is a difficulty since not everyone can discern the handwriting of different people. The described technique converts unstructured handwriting into a structured type. To resolve the problem, the authors want to separate every letter of the words so as to converting the alphabet into a commonplace type. This is to simplify the written language for everyone. This article presents a piece of text identification from image. OCR is enforced to convert electronic kind of image into machine-editable text.

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Cordeiro, Carolina, São Luís Castro, and Teresa Limpo. "Examining Potential Sources of Gender Differences in Writing: The Role of Handwriting Fluency and Self-Efficacy Beliefs." Written Communication 35, no. 4 (2018): 448–73. http://dx.doi.org/10.1177/0741088318788843.

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A growing body of scholarship in the field of writing research from a cognitive perspective suggests that girls tend to outperform boys in particular writing tasks. Still, our understanding about gender differences continues to evolve. The present study specifically focused on gender differences in writing between students from Grade 4 to Grade 9. We examined differences in handwriting and self-efficacy, as well as in three measures of written composition across two genres (viz., spelling, text length, and text quality in stories and opinion essays). Moreover, we tested whether there were diff

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Dhotre, Ketaki G., Harshali K. Ghumate, Mayuri Mane, and Prof Savita Lade. "Handwriting to Text Conversion for English Language Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 1346–51. http://dx.doi.org/10.22214/ijraset.2022.40876.

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Abstract: Because of the rising use of digital technology in all businesses and in all day-to-day activities to store and communicate information, recognition systems in writing have become a prominent study topic and development. Humans still require handwriting copies to be converted into digital copies that can be shared and preserved electronically. Handwriting recognition is one of the most active study areas, and deep neural networks are being used in it. Humans find it simple to recognise handwriting, but computers find it tough. Nowadays, technologies that detect handwriting letters, c

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Xiong, Yu-Jie, Li Liu, Shujing Lyu, Patrick S. P. Wang, and Yue Lu. "Improving Text-Independent Chinese Writer Identification with the Aid of Character Pairs." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 02 (2018): 1953001. http://dx.doi.org/10.1142/s021800141953001x.

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Text-independent Chinese writer identification does not depend on the text content of the query and reference handwritings. In order to deal with the uncertainty of the text content, text-independent approaches usually give special attention to the global writing style of handwriting, rather than the properties of each individual character or word. Thanks to the existence of high-frequency characters, some characters probably appear in both the query and reference handwritings in most cases. If character images in the query handwriting are similar to those in the reference handwriting, this qu

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Limpo, Teresa, and Rui A. Alves. "Written Language Bursts Mediate the Relationship Between Transcription Skills and Writing Performance." Written Communication 34, no. 3 (2017): 306–32. http://dx.doi.org/10.1177/0741088317714234.

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It is established that transcription skills (handwriting and spelling) constrain children’s writing. Yet, little is known about the mechanism underlying this relationship. This study examined the mediating role of bursts and pauses on the link between transcription skills and writing fluency or text quality. For that, 174 second graders did the alphabet task and wrote a story using HandSpy. Path analyses indicated that writing fluency and text quality models were excellent descriptions of the data, with 80% and 46% of explained variance, respectively. Results showed that handwriting and spelli

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Loizzo, Annalivia, Valerio Zaccaria, Barbara Caravale, and Carlo Di Brina. "Validation of the Concise Assessment Scale for Children’s Handwriting (BHK) in an Italian Population." Children 10, no. 2 (2023): 223. http://dx.doi.org/10.3390/children10020223.

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Handwriting difficulties represent a common complaint among children and may cause a significant delay in motor skills achievement. The Concise Assessment Scale for Children’s Handwriting (BHK) assesses handwriting skill in clinical and experimental settings, providing a quick evaluation of handwriting quality and speed through a copying text. The aim of the present study was to validate the Italian adaptation of the BHK in a representative primary school population. Overall, 562 children aged 7–11 from 16 public primary schools of Rome were included and asked to copy a text in 5 min using cur

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Bazarkulova, Aisaule. "KAZAKH HANDWRITING RECOGNITION." Suleyman Demirel University Bulletin Natural and Technical Sciences 62, no. 1 (2024): 88–102. https://doi.org/10.47344/sdubnts.v62i1.963.

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Recognition of handwritten text is one aspect of objectrecognition and known as handwriting detection cause of a computer’spotential to recognize and comprehend readable handwriting from resourcesincluding paper files, touch smart devices, images, etc. Data is categorized intoa number of classes or groups using pattern recognition. The paper presents asuccessful experiment in recognizing handwritten Kazakh text usingConvolutional Recurrent Neural Network based architectures and the KazakhAutonomous Handwritten Text Dataset. The proposed algorithm achieved anoverall accuracy of 86.36% and showe

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Devaraj, Anjali Yogesh, Anup S. Jain, Omisha N, and Shobana TS. "Kannada Text Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 73–78. http://dx.doi.org/10.22214/ijraset.2022.46520.

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Abstract: The task of automatic handwriting recognition is critical. This can be a difficult subject, and it has gotten a lot of attention in recent years. In the realm of picture grouping, handwritten character recognition is a problem. Handwritten characters are difficult to decipher since various people have distinct handwriting styles. For decades, researchers have been focusing on character identification in Latin handwriting. Kannada has had fewer studies conducted on it. Our "Kannada Text Recognition" research and effort attempts to classify and recognize characters written in Kannada,

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Barton, Jason J. S., Christopher J. Fox, Alla Sekunova, and Giuseppe Iaria. "Encoding in the Visual Word Form Area: An fMRI Adaptation Study of Words versus Handwriting." Journal of Cognitive Neuroscience 22, no. 8 (2010): 1649–61. http://dx.doi.org/10.1162/jocn.2009.21286.

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Written texts are not just words but complex multidimensional stimuli, including aspects such as case, font, and handwriting style, for example. Neuropsychological reports suggest that left fusiform lesions can impair the reading of text for word (lexical) content, being associated with alexia, whereas right-sided lesions may impair handwriting recognition. We used fMRI adaptation in 13 healthy participants to determine if repetition–suppression occurred for words but not handwriting in the left visual word form area (VWFA) and the reverse in the right fusiform gyrus. Contrary to these expecta

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VINITA, PATIL. "Review on Handwriting Recognition Techniques." Journal of Applied Science and Computations 5, no. 12 (2023): 1458–63. https://doi.org/10.5281/zenodo.7890740.

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- Handwriting is a skill that is unique to every individual. It was developed long time ago as a means to expand memory and facilitate communication. Handwriting of every person differs from every other. Every individual has their own style of writing. The understanding of handwriting generation is important in the development of both on-line and off-line recognition systems. Online handwriting recognition deals with information about writing dynamics as the text is being written while offline handwriting recognition deals with static information. This paper serves as a guide and updates the r

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Aliyev, Emin. "Forensic Handwriting Analysis to Determine the Psychophysiological Traits." International Journal of Religion 5, no. 6 (2024): 511–30. http://dx.doi.org/10.61707/2r6bmr11.

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In order to demonstrate the immense value and actuality of the investigations done by forensic handwriting expertises in the determination of the psychophysiological traits that lead to criminal behaviour of the performer of handwritten texts, the article analysed 2648 handwritten text samples and gave a new classification of handwriting features by dividing them into three main distinct categories. Based on the detailed forensic study of these samples, the author reaffirms that each person’s handwriting is unique and unrepeatable and becomes subconsciously adopted to the individual’s physiolo

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Ostrum, R. Brent, and Tobin A. Tanaka. "Another Look at Handwriting Movement." Journal of the American Society of Questioned Document Examiners 9, no. 2 (2006): 57–69. https://doi.org/10.69525/jasqde.131.

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Forensic document examiners usually observe and assess line quality in the written stroke without direct knowledge of the kinematics of the writing instrument used to produce it. Features in handwriting relating to line quality such as speed or pen pressure variation have been described since the days of Osborn. Qualitative assessment of such features by document examiners is routine and made according to well documented principles. In this pilot study, a digitizing tablet with an inking pen was used to measure the movement of a writing instrument as a time series of data points. Handwriting f

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Pathak, Vasundhara, Shriyansh Sharma, and Tanishka Goel. "Optical Character Recognition for Image & Handwriting to Text Conversion." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 2603–6. http://dx.doi.org/10.22214/ijraset.2022.42476.

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Abstract: This paper combines the functionality of Optical Character Recognition and speech synthesizer. The idea is to develop stoner friendly operation which performs image to text conversion. Objective The advantage of proposed system that overcomes the disadvantage of the prevailing system is that it supports multiple functionalities like editing and searching. It also adds benefit by providing heterogeneous characters recognition

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Korogodina, Maria Vladimirovna. "Variation of the script in the East-Slavonic manuscripts of the 15th – 16th centuries." Studia Slavica et Balcanica Petropolitana 2(32) (2022): 60–75. http://dx.doi.org/10.21638/spbu19.2022.205.

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The scripts of the East-Slavonic manuscripts modify significantly in the 15th and 16th centuries. New types of scripts and styles of handwriting are appeared in that time. The multiplicity of the patterns makes up many scribes to master several styles of handwriting and to vary them depending on their goals. They often use another style of handwriting for the scribe’s note. It allows a scribe to differ the main text from the information about him. Various styles of handwriting, belonged to the same scribe, were used for coding different types of text and different genres. It led to the forming

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Łuszczuk, Krystyn, Mieczysław Goc, and Andrzej Łuszczuk. "Use of scangraphy for computer visualization of handwritten text shading." Issues of Forensic Science 300 (2018): 55–66. http://dx.doi.org/10.34836/pk.2018.300.4.

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The article indicates the importance of computer programs as tools supporting the handwriting expertise and increasing its objectivity. Some of these programs are based on computer scangraphy. Scangraphy is a handwriting examination method in which a text sample is treated as a raster image (a bitmap), i.e. a set of single (dots) pixels. The bitmap pixel pattern can be tested in a variety of different ways, as required. The article describes the first computer program to use scangraphy in handwriting examination.

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Ramsey, Sandra L. "A Study of the Evolution of Handwriting from Grades Three to Six." Journal of the American Society of Questioned Document Examiners 1, no. 1 (1998): 32–39. https://doi.org/10.69525/jasqde.8.

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There are several complex factors that contribute to the uniqueness of one's writing. A research project was conducted to evaluate the validity of three long accepted premises of handwriting identification. Specimens of handwriting were obtained from one third grade class of students in the 1993-1994 school year, followed by additional handwriting specimens from these same students in grades four, five, and six. These writings were examined with regard to frequency of specific features, styles of writing, variation, and individuality. The findings support the premises of handwriting identifica

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Troia, Gary A., Julie S. Brehmer, Kaitlin Glause, Heather L. Reichmuth, and Frank Lawrence. "Direct and Indirect Effects of Literacy Skills and Writing Fluency on Writing Quality Across Three Genres." Education Sciences 10, no. 11 (2020): 297. http://dx.doi.org/10.3390/educsci10110297.

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Data were collected for this study early in the school year to analyze the direct and indirect effects of word-level literacy skills (word recognition, spelling, and written vocabulary use) and handwriting fluency on writing quality across three genres of typewritten papers. We further explored whether typing fluency and text generation fluency mediated the effects. Finally, we examined whether there was any difference in the effects across three writing genres. Fourth and fifth graders (N = 175) from 21 typical classrooms in 12 different Midwestern U.S. schools participated. Regression path a

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Anshika, Singh, and Sharvan Kumar Garg Dr. "Substantiating Precise Analysis of Data to Evaluate Students Answer Scripts." Substantiating Precise Analysis of Data to Evaluate Students Answer Scripts 8, no. 10 (2023): 4. https://doi.org/10.5281/zenodo.10057694.

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Handwriting recognition refers to interpreting and analyzing handwritten text. In rece-nt years, there have- been notable advance-ments in this field, espe-cially in the context of computerize-d assessments. As online e-xams and digital education platforms continue to gain  popularity, handwriting recognition plays a crucial role- in evaluating students' written answers. Our proposed  system automatically recognizes and scores handwritten responses on answer sheets by comparing them to the correct answers provided by a moderator. To achieve this, the system utilizes Optical Character

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Shin, Jungpil, Md Maniruzzaman, Yuta Uchida, et al. "Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods." Applied Sciences 12, no. 10 (2022): 5256. http://dx.doi.org/10.3390/app12105256.

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The classification of different age groups, such as adult and child, based on handwriting is very important due to its various applications in many different fields. In forensics, handwriting classification helps investigators focus on a certain category of writers. This paper aimed to propose a machine-learning (ML)-based approach for automatically classifying people as adults or children based on their handwritten data. This study utilized two types of handwritten databases: handwritten text and handwritten pattern, which were collected using a pen tablet. The handwritten text database had 5

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Salameh-Matar, Abeer, Naser Basal, and Naomi Weintraub. "Relationship between body functions and Arabic handwriting performance at different acquisition stages." Canadian Journal of Occupational Therapy 85, no. 5 (2018): 418–27. http://dx.doi.org/10.1177/0008417419826114.

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Background. The written languages and handwriting acquisition stages place different demands on the writer. Therefore, the relationship between body functions and handwriting performance may vary in different languages and acquisition stages; yet these demands have not been studied in the Arabic language. Purpose. We examined the relationship between linguistic, visual-motor integration (VMI), and motor coordination (MC) functions and Arabic handwriting at two handwriting acquisition stages. Method. This study used a cross-sectional and correlative design. Second- ( n = 54) and fourth-grade (

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Tan, J., J. H. Lai, P. Wang, and N. Bi. "Multiscale Region Projection Method to Discriminate Between Printed and Handwritten Text on Registration Forms." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 08 (2015): 1553005. http://dx.doi.org/10.1142/s0218001415530055.

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Techniques to identify printed and handwritten text in scanned documents differ significantly. In this paper, we address the question of how to discriminate between each type of writing on registration forms. Registration-form documents consist of various type zones, such as printed text, handwriting, table, image, noise, etc., so segmenting the various zones is a challenge. We adopt herein an approach called “multiscale-region projection” to identify printed text and handwriting. An important aspect of our approach is the use of multiscale techniques to segment document images. A new set of p

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Ms. Ancy Thomas A and Ms. Keerthi. "A Study on Handwriting Examination in Different Postures." International Journal of Scientific Research in Science and Technology 12, no. 2 (2025): 1232–37. https://doi.org/10.32628/ijsrst251222684.

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Handwriting is often used as a reliable tool in forensic investigations, especially for verifying documents and identifying individuals. However, most handwriting samples are typically collected under normal seated writing conditions. In real-life situations, people may write under unusual postures, which can influence the way their handwriting appears. This study focuses on analyzing handwriting written in three specific postures: standing against a wall, lying down, and while travelling. Participants were asked to write a fixed text in each of these positions, and the resulting samples were

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Havaei, Naser, Mahdieh Mahmodzadeh, Akram Azad, et al. "Handwriting status among Iranian primary school students: a cross-sectional study." International Journal of Therapy and Rehabilitation 28, no. 2 (2021): 1–9. http://dx.doi.org/10.12968/ijtr.2020.0007.

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Background/Aims Handwriting difficulties among school students are the primary reason for referral to school-based occupational therapy. Appropriate criteria and a complete view of the students' handwriting status are lacking in Iran. The purpose of this study was to gather comprehensive information about handwriting issues for therapists and related disciplines in Iran. Methods In this cross-sectional study, the handwriting status of 1262 Persian-language elementary school students (grades 2 and 3) from governmental schools was surveyed using the Persian handwriting assessment tool. Results T

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Maddineni, Bhavyasri. "Various Models for the Conversion of Handwritten Text to Digital Text." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 2894–99. http://dx.doi.org/10.22214/ijraset.2021.35616.

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Handwritten Text Recognition (HTR) also known as Handwriting Recognition (HWR) is the detection and interpretation of handwritten text images by the computer. Handwritten text from various sources such as notebooks, documents, forms, photographs, and other devices can be given to the computer to predict and convert into the Computerized Text/Digital Text. Humans find easier to write on a piece of paper rather than typing, but now-a-days everything is being digitalized. So, HTR/HWR has an increasing use these days. There are various techniques used in recognizing the handwriting. Some of the tr

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Bird, Carolyne, Reinoud D. Stoel, Bryan Found, and Douglas Rogers. "Skill Characteristics of Forensic Handwriting Examiners Associated with Simulated Handwritten Text." Journal of the American Society of Questioned Document Examiners 14, no. 2 (2011): 29–34. https://doi.org/10.69525/jasqde.181.

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The assessment of the process of production of handwriting (naturally written, disguised or simulated) is an important step in forensic examinations and may impact on any authorship opinion offered. However, there is currently little empirical data on the skill of forensic handwriting examiners in discriminating between disguised and simulated writing processes. The results reported here form part of a larger investigation to that end. The trial consisted of 100 pairs of handwriting samples, each with a naturally written comparison sample, and a questioned sample that was either disguised by t

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Bhattarai, Dipesh, and Pawan Kumar Sharma. "Enhancing Handwritten Text Recognition Performance with Encoder Transformer Models." Journal of Engineering Issues and Solutions 4, no. 1 (2025): 456–59. https://doi.org/10.3126/joeis.v4i1.81606.

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Handwritten Text Recognition (HTR) is a critical area in computer vision and natural language processing, aiming to convert handwritten content into machine-readable text. The task poses significant challenges due to the inherent variability in handwriting styles, stroke patterns, character spacing, and writing instruments. Traditional HTR techniques, often based on statistical models or shallow neural networks, frequently struggle to generalize across diverse handwriting samples, leading to suboptimal performance in real-world applications. This improvement demonstrates a relative gain of app

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Ram Kumar, R. P., A. Chandra Prasad, K. Vishnuvardhan, K. Bhuvanesh, and Sanjeev Dhama. "Automated Handwritten Text Recognition." E3S Web of Conferences 430 (2023): 01022. http://dx.doi.org/10.1051/e3sconf/202343001022.

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A computer’s capacity to recognize and convert handwritten inputs from sources like photographs and paper documents into digital format is known as Automated Handwritten Text Recognition (AHTR). Systems for reading handwriting are frequently employed in a variety of fields, including banking, finance, and the healthcare industry. In this paper, we took on the problem of categorizing any handwritten artwork, whether it be in block lettering or cursive. There are many different types of handwritten characters, including digits, symbols, and scripts in both English and other languages. This makes

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Sharma, Kartik, S. V. Jagadeesh Kona, Anshul Jangwal, Aarthy M, Prayline Rajabai C, and Deepika Rani Sona. "Handwritten Digits and Optical Characters Recognition." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (2023): 20–24. http://dx.doi.org/10.17762/ijritcc.v11i4.6376.

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The process of transcribing a language represented in its spatial form of graphical characters into its symbolic representation is called handwriting recognition. Each script has a collection of characters or letters, often known as symbols, that all share the same fundamental shapes. Handwriting analysis aims to correctly identify input characters or images before being analysed by various automated process systems. Recent research in image processing demonstrates the significance of image content retrieval. Optical character recognition (OCR) systems can extract text from photographs and tra

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Tarasova, Nataliya, and Tatyana Panyukova. "Semantics and Ideography of Dostoevsky's Handwritten Text: from Handwriting to Meaning." Неизвестный Достоевский 7, no. 4 (2020): 222–91. http://dx.doi.org/10.15393/j10.art.2020.5081.

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The paper analyzes the material from two workbooks (1864–1867) that belonged to Feodor Dostoevsky (Russian State Archive of Literature and Art. Collection 212, inventory 1, storage units 4 and 5). The research was conducted in three main areas: selection of information for the letter style alphabet; comparative analysis of sources (manuscripts and publications of handwritten text) for the purpose of identifying and correcting errors in its reading; a review of calligraphy and graphics of Feodor Dostoevsky. The alphabet of letter styles allows to establish typical and atypical letter styles in

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Dhandra, B. V., and M. B. Vijayalaxmi. "A Novel Approach to Text Dependent Writer Identification of Kannada Handwriting." Procedia Computer Science 49 (2015): 33–41. http://dx.doi.org/10.1016/j.procs.2015.04.224.

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Mustofa, Dedi. "KEMAMPUAN BAHASA ARAB DALAM PANDANGAN PERILAKU KEAGAMAAN DAN KEMAMPUAN MENULIS ARAB (KHAT)." Lisanan Arabiya: Jurnal Pendidikan Bahasa Arab 3, no. 01 (2019): 1–14. http://dx.doi.org/10.32699/liar.v3i01.994.

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Writing is one of the very penting.melalui language skills to write someonewill be able to more easily convey the message in writing and can undersandand remember the message from a script or text. However, writing the Arabiclanguage itself is inseparable from the ability to maser handwriting. Without anundersanding of khat good, allows a person to experience an error in writing theArabic alphabet even in undersanding the message of an Arabic text. The purposeof this sudy was to determine how much inﬂuence the ability of the Masery ofArabic handwriting and religious behavior of a person.

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Gan, Ji, and Weiqiang Wang. "HiGAN: Handwriting Imitation Conditioned on Arbitrary-Length Texts and Disentangled Styles." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (2021): 7484–92. http://dx.doi.org/10.1609/aaai.v35i9.16917.

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Given limited handwriting scripts, humans can easily visualize (or imagine) what the handwritten words/texts would look like with other arbitrary textual contents. Moreover, a person also is able to imitate the handwriting styles of provided reference samples. Humans can do such hallucinations, perhaps because they can learn to disentangle the calligraphic styles and textual contents from given handwriting scripts. However, computers cannot study to do such flexible handwriting imitation with existing techniques. In this paper, we propose a novel handwriting imitation generative adversarial ne

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Almisreb, Ali Abd, Nooritawati Md Tahir, Sherzod Turaev, Mohammed A. Saleh, and Syed Abdul Mutalib Al Junid. "Arabic Handwriting Classification using Deep Transfer Learning Techniques." Pertanika Journal of Science and Technology 30, no. 1 (2022): 641–54. http://dx.doi.org/10.47836/pjst.30.1.35.

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Arabic handwriting is slightly different from the handwriting of other languages; hence it is possible to distinguish the handwriting written by the native or non-native writer based on their handwriting. However, classifying Arabic handwriting is challenging using traditional text recognition algorithms. Thus, this study evaluated and validated the utilisation of deep transfer learning models to overcome such issues. Hence, seven types of deep learning transfer models, namely the AlexNet, GoogleNet, ResNet18, ResNet50, ResNet101, VGG16, and VGG19, were used to determine the most suitable mode

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Heijebu, V. Suresh, Bhupendra Singh, Shrikant Srivastava, and Shivendra Kumar Singh. "A study of handwriting sample in geriatric population with cognitive impairment." Journal of Geriatric Mental Health 9, no. 2 (2022): 79–86. http://dx.doi.org/10.4103/jgmh.jgmh_39_22.

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Objectives: Cognitive impairment in the geriatric population often remains undiagnosed until progressed enough to cause interruptions in activities of daily living. Routine tests are time taking, requiring a specialist. Handwriting function reflects the brain's cognitive capacity by involving it's both halves. It is easy to collect and does not strain the participant, and can aid in the faster diagnosis of cognitive impairment. Materials and Methods: To study handwriting parameters collected with Livescribe Echo Smart Pen and compare them with cognitive scores of Montreal Cognitive Assessment-

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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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Al-Maadeed, Somaya. "Text-Dependent Writer Identification for Arabic Handwriting." Journal of Electrical and Computer Engineering 2012 (2012): 1–8. http://dx.doi.org/10.1155/2012/794106.

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This paper proposes a system for text-dependent writer identification based on Arabic handwriting. First, a database of words was assembled and used as a test base. Next, features vectors were extracted from writers' word images. Prior to the feature extraction process, normalization operations were applied to the word or text line under analysis. In this work, we studied the feature extraction and recognition operations of Arabic text on the identification rate of writers. Because there is no well-known database containing Arabic handwritten words for researchers to test, we have built a new

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Mangas Afonso, Hector, Anouk Keizer, Peter Biľak, and Sofie Beier. "Familiarity Effect in the Perception of Handwriting: Evaluating in-group/out-group effect among readers of the Latin script." Journal of Writing Research 16, no. 2 (2024): 203–22. http://dx.doi.org/10.17239/jowr-2024.16.02.01.

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There is much evidence that familiarity can affect perception of stimuli, with items that are familiar to the individual being preferred and better remembered. Previous research has also shown that familiarity with a typeface increases preference for it, but no studies have evaluated the impact of familiarity in relation to the affect towards handwritten text. For the present study, a two-part experiment (N = 422) was designed to measure how contemporary users of the Latin script perceive handwritten text. The first section was designed to collect specimens of the participants’ handwriting. Th

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Fang, Fengyi, Hongwei Zhang, Lishuang Zhan, et al. "Handwriting Velcro." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 4 (2022): 1–31. http://dx.doi.org/10.1145/3569461.

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Text input is a desired feature for AR glasses. While there already exist various input modalities (e.g., voice, mid-air gesture), the diverse demands required by different input scenarios can hardly be met by the small number of fixed input postures offered by existing solutions. In this paper, we present Handwriting Velcro, a novel text input solution for AR glasses based on flexible touch sensors. The distinct advantage of our system is that it can easily stick to different body parts, thus endowing AR glasses with posture-adaptive handwriting input. We explored the design space of on-body

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Tran, Dat, Wanli Ma, and Dharmendra Sharma. "Handwriting Recognition Applications for Tablet PCs." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 7 (2007): 787–92. http://dx.doi.org/10.20965/jaciii.2007.p0787.

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This paper presents handwriting recognition applications developed and tested on the tablet PC – a new generation of notebook computers. Users write on a tablet PC screen with a tablet pen and a built-in user-independent handwriting recognition tool converts handwritings to printed text. We present handwriting recognition applications using the built-in recognition tool and signature verification using our own verification tool based on fuzzy c-means vector quantization (FCMVQ) and observable Markov modeling (OMM). Experimental results for the signature verification system are also presented.

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Jaiswal, Kunal, Avichal Suneja, Aman Kumar, Anany Ladha, and Nidhi Mishra. "Preprocessing Low Quality Handwritten Documents for OCR Models." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2980–85. http://dx.doi.org/10.22214/ijraset.2023.50664.

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Abstract: Handwriting recognition using OCR (Optical Character Recognition) is a transformative technology that is rapidly changing the way we interact with handwritten documents. OCR technology has traditionally been used for scanning printed text, but with advancements in machine learning and computer vision, it is now possible to recognize and digitize handwritten text as well. This has immense practical implications, as it enables handwritten notes, letters, and documents to be easily searchable, editable, and shareable in digital formats[1]. However, despite its potential, handwriting rec

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Yeleussinov, Arman, Yedilkhan Amirgaliyev, and Lyailya Cherikbayeva. "Improving OCR Accuracy for Kazakh Handwriting Recognition Using GAN Models." Applied Sciences 13, no. 9 (2023): 5677. http://dx.doi.org/10.3390/app13095677.

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This paper aims to increase the accuracy of Kazakh handwriting text recognition (KHTR) using the generative adversarial network (GAN), where a handwriting word image generator and an image quality discriminator are constructed. In order to obtain a high-quality image of handwritten text, the multiple losses are intended to encourage the generator to learn the structural properties of the texts. In this case, the quality discriminator is trained on the basis of the relativistic loss function. Based on the proposed structure, the resulting document images not only preserve texture details but al

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