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1

Ziviani, Jenny. "Use of Modern Cursive Handwriting and Handwriting Speed for Children Ages 7 to 14 Years." Perceptual and Motor Skills 82, no. 1 (February 1996): 282. http://dx.doi.org/10.2466/pms.1996.82.1.282.

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2

Karlsdottir, Ragnheidur. "Development of Cursive Handwriting." Perceptual and Motor Skills 82, no. 2 (April 1996): 659–73. http://dx.doi.org/10.2466/pms.1996.82.2.659.

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The efficiency of two different instructional methods for improving the growth of handwriting quality in the upper grades of primary school was investigated in field experiments using a control-group design. No improvement in mean quality was found for a group of 38 children using a method based on copying exercises. A substantial improvement in mean quality, corresponding to about 1.4 SD, was found for 23 children using a method based on reintroduction of the letter forms explaining each form visually and verbally. It was concluded that the bottleneck in the development of handwriting quality in primary school is a mismatch between the instructional methods used and the perceptual ability of the children.
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3

Bo, Jin, Julia Barta, Hilary Ferencak, Sara Comstock, Vanessa Riley, and Joni Krueger. "Developmental Characteristics in Cursive and Printed Letter-Writing for School-Age Children." Journal of Motor Learning and Development 2, no. 1 (March 2014): 1–8. http://dx.doi.org/10.1123/jmld.2012-0001.

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The current study evaluated the developmental characteristics of printed and cursive letter writing in early school-age children. We predicted fewer age-related changes on spatial and temporal measures in cursive letter writing due to lower explicit timing demands compared with printed letter writing. Thirty children wrote the letters e and l in cursive and printed forms repetitively. For printed letters, significant age effects were seen in temporal consistency, whereas cursive letters showed age-related improvement in spatial consistency. Children tended to have higher consistency for printed handwriting than they did for cursive writing. Because of an overall advantage for printed handwriting, the explicit timing hypothesis was not fully supported. We argue that experiential factors influence the development of handwriting.
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4

Huang, Tongcheng, Siyang Zhang, Xu Duan, and Ronglong Liang. "On-Line Handy Handwriting Chinese Characters Input for Non-Chinese Speakers Based on Wavelet Neural Network." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (May 9, 2016): 1659017. http://dx.doi.org/10.1142/s0218001416590175.

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Non-Chinese speakers hold increasing opportunities and need to process Chinese information and communicate in Chinese. This paper, with the purpose of facilitating the handwriting input of Chinese characters for non-Chinese speakers, is directed towards the development of the handwriting rules and vocabulary for Latin-style anti-cursive characters and the ways of their selection and classification. This aims to build a practical platform by utilizing three characteristics of wavelet neural network — automatically ascertaining the number of hidden layer unit, converging rapidly and never running into the partial minimum of networks — for a simple Latin-style online handwriting input and processing, meanwhile, taking the customary handwriting habits of non-Chinese speakers. The paper, based on profound information of cursive characters, deciphered the genetic code of ancient cursive symbols and made clear the rules for characters changing into its cursive style. As a result, it breaks the bottleneck, which enables non-Chinese speakers to easily input information through handwriting Chinese characters.
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Fitrianingsih, Fitrianingsih, Sarifuddin Madenda, Ernastuti Ernastuti, Suryarini Widodo, and Rodiah Rodiah. "Cursive Handwriting Segmentation using Ideal Distance Approach." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (October 1, 2017): 2863. http://dx.doi.org/10.11591/ijece.v7i5.pp2863-2872.

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Offline cursive handwriting becomes a major challenge due to the huge amount of handwriting varieties such as slant handwriting, space between words, the size and direction of the letter, the style of writing the letter and handwriting with contour similarity on some letters. There are some steps for recursive handwriting recognition. The steps are preprocessing, morphology, segmentation, features of letter extraction and recognition. Segmentation is a crucial process in handwriting recognition since the success of segmentation step will determine the success level of recognition. This paper proposes a segmentation algorithm that segment recursive handwriting into letters. These letters will form words using a method that determine the intersection cutting point of image recursive handwriting with an ideal image distance. The ideal distance of recursive handwriting image is an ideal distance segmentation point in order to avoid the cutting of other letter’s section. The width and height of images are used to determine the accurate segmentation point. There were 999 recursive handwriting input images taken from 25 researchers used for this study. The images used are the images obtained from preprocessing step. Those are the images with slope correction. This study used Support Vector Machine (SVM) to recognize recursive handwriting. The experiments show the proposed segmentation algorithm able to segment the image precisely and have 97% success recognizing the recursive handwriting.
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6

Karlsdottir, Ragnheidur. "Comparison of Cursive Models for Handwriting Instruction." Perceptual and Motor Skills 85, no. 3_suppl (December 1997): 1171–84. http://dx.doi.org/10.2466/pms.1997.85.3f.1171.

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The efficiency of four different cursive handwriting styles as model alphabets for handwriting instruction of primary school children was compared in a cross-sectional field experiment from Grade 3 to 6 in terms of the average handwriting speed developed by the children and the average rate of convergence of the children's handwriting to the style of their model. It was concluded that styles with regular entry stroke patterns give the steadiest rate of convergence to the model and styles with short ascenders and descenders and strokes with not too high curvatures give the highest handwriting speed.
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7

Zou, Mingfu. "On-Line Handwriting Cursive Recognition." Journal of Computer Research and Development 43, no. 1 (2006): 138. http://dx.doi.org/10.1360/crad20060122.

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8

Singer, Y., and N. Tishby. "Dynamical encoding of cursive handwriting." Biological Cybernetics 71, no. 3 (August 1, 1994): 227–37. http://dx.doi.org/10.1007/s004220050085.

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9

Singer, Y., and N. Tishby. "Dynamical encoding of cursive handwriting." Biological Cybernetics 71, no. 3 (July 1994): 227–37. http://dx.doi.org/10.1007/bf00202762.

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10

Morin, Marie-France, Natalie Lavoie, and Isabelle Montésinos-Gelet. "The Effects of Manuscript, Cursive or Manuscript/Cursive Styles on Writing Development in Grade 2." Language and Literacy 14, no. 1 (January 25, 2012): 110. http://dx.doi.org/10.20360/g21s3v.

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In the research area of writing development, an increasing number of researchers suggest that graphomotor skills could be much more important than they appear to be (Christensen, 2009). Few researchers have studied the link between handwriting and teaching practices, despite the fact that some studies indicate its importance (Graham, 2010). The general objective of this study is to explore the relationship between different handwriting styles and the development of writing skills among 715 children in Grade 2. Generally, our results show that the three handwriting styles (manuscript/cursive, manuscript, and cursive) have different effects on writing development (speed, quality, word production, and text production).
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11

Lee, Do-Hoon, and Hwan-Gue Cho. "A New Synthesizing Method for Handwriting Korean Scripts." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 01 (February 1998): 45–61. http://dx.doi.org/10.1142/s0218001498000051.

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So far there have been models for generating handwriting scripts of western languages. Since the basic structure of oriental languages such as Chinese, Japanese and Korean, is quite different from that of alphabet-based western languages, western handwriting models cannot be applied directly to oriental languages except for a few models. In this paper, we propose a new generation model for generating Korean handwriting scripts. For this we choose 11 characteristic parameters which determine the shape of handwriting scripts. These parameters were selected experimentally by investigating ample Korean scripts. Furthermore we propose the Beta-velocity model to simulate the cursive stroke movement by manipulating curvilinear and cursive connection for consecutive strokes within a letter or a word. In order to generate these oriental cursive strokes, we adopted Beta distribution curve basis which enables one to make easy asymmetric velocity profile. By combining all these features, we synthesize various handwriting Korean scripts by controlling 11 characteristic parameters. In the final section of this paper, we give experimental results. These easy-to-synthesize Korean scripts can be used in evaluating off-line character recognition system and a word processor by supporting handwriting-style fonts.
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12

Arfé, Barbara, Francesca Corato, Eleonora Pizzocaro, and Anne Merella. "The Effects of Script and Orthographic Complexity on the Handwriting and Spelling Performance of Children With Dyslexia." Journal of Learning Disabilities 53, no. 2 (December 11, 2019): 96–108. http://dx.doi.org/10.1177/0022219419892845.

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Handwriting and spelling problems are often associated in dyslexia. However, the nature of their association is still unclear, and most of the existing research in this area is on deep orthographies (mainly English). The extent to which findings are applicable across languages is uncertain. This article examines the effects of script (manuscript/cursive) and orthographic complexity (complex/simple spellings) on the word dictation and word-copying performance of a group of 24 Italian children with dyslexia and handwriting difficulties (DH group, aged 8–10). Their performance was compared with that of a chronologically age–matched group (CA) and a group of younger children matched to the DH group for their handwriting skills (HA: handwriting age group). Children performed two classical handwriting tasks: the alphabet task and a sentence-copy task, and dictation and copy tasks of orthographically complex words and orthographically simple words. Copying was performed in manuscript and cursive. The results show that although the DH group shows a significant deficit in graphomotor processes, orthographic complexity more than the visual-motor characteristics of the task (i.e., script) affects their performance in handwriting. An advantage for cursive script in DH children, but not in the other two groups, emerges from the study.
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13

Arica, N., and F. T. Yarman-Vural. "Optical character recognition for cursive handwriting." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 6 (June 2002): 801–13. http://dx.doi.org/10.1109/tpami.2002.1008386.

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14

Arslan, Derya, and Zeynep Karatas. "Cursive Handwriting Anxiety Scale for Teachers." Procedia - Social and Behavioral Sciences 46 (2012): 5146–52. http://dx.doi.org/10.1016/j.sbspro.2012.06.398.

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15

Abuhaiba, I. S. I., M. J. J. Holt, and S. Datta. "Recognition of Off-Line Cursive Handwriting." Computer Vision and Image Understanding 71, no. 1 (July 1998): 19–38. http://dx.doi.org/10.1006/cviu.1997.0629.

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16

Morasso, Pietro, and Vittorio Sanguineti. "Neurocomputing aspects in modelling cursive handwriting." Acta Psychologica 82, no. 1-3 (March 1993): 213–35. http://dx.doi.org/10.1016/0001-6918(93)90013-h.

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17

Kučera, Miloš. "On Writing and Handwriting." Journal of Pedagogy / Pedagogický casopis 1, no. 2 (January 1, 2010): 11–28. http://dx.doi.org/10.2478/v10159-010-0007-4.

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On Writing and Handwriting Writing is often considered secondary to the spoken language, as it is only coded sound-by-sound. But other scholars have demonstrated that writing is similar to ‘arithmetic’: a cognitive structuring, a shift to the meta-level (‘for the eye’). Handwriting (referred to here as the cursive writing in the sense of joined up handwriting, of ‘écriture liée’) differs from writing (in the first analysis): it has its own grammar composed of paradigmatic gestemes and tracemes and its own syntagmatic rules that connect them. In emotional terms, handwriting is designed to provide a special pleasure by its own drive (instinct, ‘Trieb’). But there is also cognitive aspect to it: the rapidity and fluidity of a cursive writing could be (in professional writing, for instance) more important (at the climax of the creative process) than it being legible for all eternity. The project of the new handwriting reform for Czech schools, abolishing the liaison between letters, is shown to be a modern and technically simplified form of calligraphy.
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18

Kaur, Amrit Veer, and Amandeep Verma. "Hybrid Wavelet based Technique for Text Extraction from Images." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 9 (October 31, 2017): 24. http://dx.doi.org/10.23956/ijarcsse.v7i9.406.

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This paper reviews the current state of the art in handwriting recognition research. The paper deals with issues such as hand-printed character and cursive handwritten word recognition. It describes recent achievements, difficulties, successes and challenges in all aspects of handwriting recognition.
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19

Senior, A. W., and A. J. Robinson. "An off-line cursive handwriting recognition system." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 3 (March 1998): 309–21. http://dx.doi.org/10.1109/34.667887.

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20

Şara Hürsoy, Perihan. "Transition from the rules of cursive italic handwriting to manuscript handwriting regarding the views of teacher, parent and studentBitişik eğik yazıdan dik temel yazı kullanımına geçiş ile ilgili öğretmen, veli ve öğrenci görüşleri." Journal of Human Sciences 14, no. 4 (January 1, 2018): 4809. http://dx.doi.org/10.14687/jhs.v14i4.5144.

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There are various views about the use of manuscript handwriting and cursive italic handwriting in the teaching early years literacy. The reason of this research is to explore the thoughts of the first class teachers, parents who have children in the first class and students who are receiving education in the first class about the transition from cursive italic handwriting to manuscript handwriting.The technique of interview is used in this research as one of the qualitative research methods. 20 first class teachers in duty, 20 parents and 20 first class students, which make up total of 60 persons, participated to this research in the province of Uşak. The data are compiled partially by structured interview forms and analysed descriptively. According to findings, first class teachers who participated to this research are having positive thoughts about the manuscript handwriting. The majority of class teachers who participated to this research expressed their difficulties in teaching the cursive italic handwriting.Class teachers who participated to this research prefer the teaching early years literacy through manuscript letters to the teaching early years literacy through the cursive italic handwriting.The majority of class teachers, parents and students who participated in this research considered the decision about the transition from the cursive italic handwriting to manuscript handwriting in the education of first reading and writing during the academic year of 2016-2017 as a true and appropriate decision.Parents and students participated to the research are preferring manuscript handwriting in the education of first reading and writing.Extended English abstract is in the end of Full Text PDF (TURKISH) file. Özetİlk okuma yazma öğretiminde dik temel harflerin kullanımı ve bitişik eğik yazının kullanılması yönünde çeşitli görüşler bulunmaktadır. Bu araştırmanın amacı birinci sınıf öğretmenlerinin, birinci sınıfta çocuğu bulunan velilerin ve birinci sınıfta öğrenim gören öğrencilerin bitişik eğik yazıdan dik yazıya geçiş ile ilgili düşüncelerini ortaya çıkarmaktır.Araştırmada nitel araştırma yöntemlerinden görüşme tekniği kullanılmıştır. Araştırmaya Uşak ilinde görev yapmakta olan 20 birinci sınıf öğretmeni, 20 veli ve 20 birinci sınıf öğrencisi olmak üzere 60 kişi katılmıştır. Yarı yapılandırılmış görüşme formu ile veriler toplanmış ve betimsel olarak analiz edilmiştir.Araştırma sonuçlarına göre; sınıf öğretmenleri dik temel harflerin öğretimi ile ilgili olumlu düşüncelere sahiptirler. Birinci sınıf öğretmenlerinin büyük çoğunluğu bitişik eğik yazı öğretiminde zorlandıklarını dile getirmişlerdir. Araştırmaya katılan sınıf öğretmenleri dik temel harflerle ilk okuma yazma öğretimini bitişik eğik yazı ile ilk okuma yazma öğretimine tercih etmektedirler.Araştırmaya katılan sınıf öğretmenlerinin büyük çoğunluğu, veliler ve öğrenciler 2016-2017 eğitim öğretim yılında ilk okuma yazma öğretiminde bitişik eğik yazıdan, dik temel harflere geçiş hakkındaki kararı doğru ve yerinde bir karar olarak nitelendirmişlerdir.Araştırmaya katılan veliler ve öğrenciler ilk okuma yazma öğretiminde dik temel yazıyı tercih etmektedirler.
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21

Hayes, William N. "Identifying Sex from Handwriting." Perceptual and Motor Skills 83, no. 3 (December 1996): 791–800. http://dx.doi.org/10.2466/pms.1996.83.3.791.

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The ability to infer the sex of the writer from cursive handwriting was examined under a range of conditions. In 5 experiments male and female college students were able to perform this task at the 75% accuracy level even with small amounts of material, sometimes only a single letter or a single geometric pattern. On the other hand, age of the writer was just barely discernible from handwriting. It was suggested that sex or gender is present in handwriting in much the same way as it is present in movement of the whole body.
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22

DE STEFANO, CLAUDIO, GIANLUCA GUADAGNO, and ANGELO MARCELLI. "A SALIENCY-BASED SEGMENTATION METHOD FOR ONLINE CURSIVE HANDWRITING." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1139–56. http://dx.doi.org/10.1142/s021800140400368x.

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We propose a model for the segmentation of cursive handwriting into strokes that has been derived in analogy with those proposed in the literature for early processing tasks in primate visual system. The model allows reformulating the problem of selecting on the ink the points corresponding to perceptually relevant changes of curvature as a preattentive, purely bottom-up visual task, where the conspicuity of curvature changes is measured in terms of their saliency. The modeling of the segmentation as a saliency-driven visual task has lead to a segmentation algorithm whose architecture is biologically-plausible and that does not rely on any parameter other than those that can be directly obtained from the ink. Experimental results show that the performance is very stable and predictable, thus preventing those erratic behaviors of segmentation methods often reported in the literature. They also suggest that the proposed measure of saliency has a direct relation with the dynamics of the handwriting, so as it could be used to capture in a quantitative way some aspects of cursive handwriting intuitively related to the notion of style.
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23

DE STEFANO, CLAUDIO, and ANGELO MARCELLI. "AN EFFICIENT METHOD FOR ONLINE CURSIVE HANDWRITING STROKES REORDERING." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1157–71. http://dx.doi.org/10.1142/s0218001404003691.

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In the framework of online cursive handwriting recognition, we present an efficient method for reordering the sequence of strokes composing handwriting in two special cases of interest: the horizontal bar of the character "" and the dot of the character "". The proposed method exploits shape information for selecting the strokes that most likely correspond to the features of interest, and layout and topological information for locating the strokes representing the body of the characters to which the features belong to. The method does not depend on the specific algorithm used for detecting the elementary strokes in which the electronic ink may be decomposed into. The performance of our method, evaluated on a data set of cursive words produced by 50 different writers, has shown a correct reordering of the sequence in more than 85% of the cases. Thus, the proposed method allows obtaining a more stable and invariant description of the electronic ink in terms of elementary stroke sequences, and therefore can be helpfully used as a preprocessing step for both segmentation-based and word-based handwriting recognition systems.
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24

Lee, Hong, and Brijesh Verma. "Binary Validation as Segmentation for Cursive Handwriting Recognition." Asian Journal of Information Technology 10, no. 5 (May 1, 2011): 180–91. http://dx.doi.org/10.3923/ajit.2011.180.191.

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25

Lee, Hong, and Brijesh Verma. "Binary segmentation algorithm for English cursive handwriting recognition." Pattern Recognition 45, no. 4 (April 2012): 1306–17. http://dx.doi.org/10.1016/j.patcog.2011.09.015.

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26

Talbert-Johnson, Carolyn, Erin Salva, William J. Sweeney, and John O. Cooper. "Cursive Handwriting: Measurement of Function Rather Than Topography." Journal of Educational Research 85, no. 2 (November 1991): 117–24. http://dx.doi.org/10.1080/00220671.1991.10702821.

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27

Almuallim, Hussein, and Shoichiro Yamaguchi. "A Method of Recognition of Arabic Cursive Handwriting." IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-9, no. 5 (September 1987): 715–22. http://dx.doi.org/10.1109/tpami.1987.4767970.

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28

Edelman, Shimon, Tamar Flash, and Shimon Ullman. "Reading cursive handwriting by alignment of letter prototypes." International Journal of Computer Vision 5, no. 3 (December 1990): 303–31. http://dx.doi.org/10.1007/bf00126503.

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29

Janssen, Jac J. "On Style in Egyptian Handwriting." Journal of Egyptian Archaeology 73, no. 1 (August 1987): 161–67. http://dx.doi.org/10.1177/030751338707300112.

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The author attempts to demonstrate, on the basis of the hieratic forms of pȝ in the Late Ramesside Letters, that it is possible to distinguish the handwriting of various scribes. Although many similar forms occur, particularly very cursive ones, there appears to be a statistical difference between the forms used by Dhutmose, Butehamun, and the scribe of the General Kenkhnum. When extended to other groups of signs and other manuscripts, the method described may help to date otherwise undatable texts.
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Andrews, Jane E., and Linda J. Lombardino. "Strategies for Teaching Handwriting to Children with Writing Disabilities." Perspectives on Language Learning and Education 21, no. 3 (July 2014): 114–26. http://dx.doi.org/10.1044/lle21.3.114.

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Handwriting abilities have been shown to play an important role in the academic performance of children, especially children who have disorders of written language. Dysgraphia is the most commonly identified neurodevelopmental grapho-motor disorder of handwriting and it often co-occurs with dyslexia. Even in an age where much print is accomplished by keyboarding, legible handwriting continues to be an indispensable skill for a range of academic, vocational, social, and professional activities. In keeping with research that supports the importance of cursive writing, this paper focuses on guidelines for speech-language pathologists (SLPs) who evaluate, diagnose, and treat children with disorders of written language accompanied by marked deficits in handwriting.
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31

Morales, Celia, Verónica Gil, Natalia Suárez, Desireé González, and Juan E. Jiménez. "FLUIDEZ Y EXACTITUD EN LA COPIA DE LETRAS DEL ALFABETO (MANUSCRITA VS. CURSIVA): UN ESTUDIO TRANSVERSAL." International Journal of Developmental and Educational Psychology. Revista INFAD de Psicología. 6, no. 1 (January 12, 2017): 485. http://dx.doi.org/10.17060/ijodaep.2014.n1.v6.768.

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Abstract.FLUENCY AND ACCURACY IN COPYING TASKS (MANUSCRIPT VS. CURSIVE): A CROSS-SECTIONAL STUDYHandwriting evolves with the pass of time. The type of script which children begin to learn depends on curriculum in their countries and the educational policy. There are two main types of script: manuscript and cursive. There is a controversial issue about which type of script would be best to use to begin the teaching of handwriting, but has not been a consensus yet. This research analyzes manuscript and cursive script modalities. Our objective was to determine whether there are differences in accuracy and fluency when students are copying the alphabet letters using different types of script (manuscript vs. cursive), and also whether these differences are mediated by the grade (1st, 2nd and 3rd ). A subtest from the test called Early Grade Writing Assessment (EGWA) (Jiménez, 2012) was administered to a sample of children from 1st, 2nd and 3rd grade.Keywords: writing, fluency, accuracy, manuscript letter, cursive letter, cross-sectional study.Resumen.La escritura evoluciona con el paso del tiempo. El tipo de letra que los niños comienzan a aprender depende del currículo de su país y la política educativa. Hay dos tipos principales de letra: manuscrita y cursiva. Existe un debate sobre qué tipo de letra sería mejor utilizar para comenzar la enseñanza de la escritura, pero no se ha llegado a un consenso. Esta investigación analiza la escritura con ambos tipos de letra. Nuestro objetivo ha sido averiguar si existen diferencias en la exactitud y fluidez cuando los niños copian el alfabeto utilizando diferentes tipos de letras (manuscrita vs. cursiva), y si estas diferencias están mediatizadas por el curso (1º, 2º y 3º). Para ello se administraron algunos subtests de la prueba denominada Early Grade Writing Assessment (EGWA) (Jiménez, 2012) a una muestra de niños de 1º, 2º y 3º de Educación Primaria.Palabras clave: escritura, fluidez, exactitud, letra manuscrita, letra cursiva, diseño transversal.
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Cohen, Marsha R. "Individual and Sex Differences in Speed of Handwriting among High School Students." Perceptual and Motor Skills 84, no. 3_suppl (June 1997): 1428–30. http://dx.doi.org/10.2466/pms.1997.84.3c.1428.

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A group of 153 high school students participated in a cursive handwriting task which required them to copy sentences as quickly as possible. The 78 girls performed significantly better than the 75 boys and a substantial range of speed for each sex was found. The results have implications for more demanding writing and composition processes. Handwriting speed has the potential to act as a limiting factor under some circumstances.
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VARGA, T., and H. BUNKE. "OFFLINE HANDWRITING RECOGNITION USING SYNTHETIC TRAINING DATA PRODUCED BY MEANS OF A GEOMETRICAL DISTORTION MODEL." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (November 2004): 1285–302. http://dx.doi.org/10.1142/s0218001404003666.

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A perturbation model for the generation of synthetic textlines from existing cursively handwritten lines of text produced by human writers is presented. The goal of synthetic textline generation is to improve the performance of an offline cursive handwriting recognition system by providing it with additional training data. It can be expected that by adding synthetic training data the variability of the training set improves, which leads to a higher recognition rate. On the other hand, synthetic training data may bias a recognizer towards unnatural handwriting styles, which could lead to a deterioration of the recognition rate. In this paper the proposed perturbation model is evaluated under several experimental conditions, and it is shown that significant improvement of the recognition performance is possible even when the original training set is large and the textlines are provided by a large number of different writers.
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Fujioka, Hiroyuki, and Hiroyuki Kano. "B-20 Constructing Cursive Character Fonts from Human Handwriting Motion with Esthetic Evaluation." Proceedings of Joint Symposium: Symposium on Sports Engineering, Symposium on Human Dynamics 2009 (2009): 329–32. http://dx.doi.org/10.1299/jsmesports.2009.0_329.

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35

YANIKOGLU, BERRIN, and PETER A. SANDON. "SEGMENTATION OF OFF-LINE CURSIVE HANDWRITING USING LINEAR PROGRAMMING." Pattern Recognition 31, no. 12 (December 1998): 1825–33. http://dx.doi.org/10.1016/s0031-3203(98)00081-8.

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Eldridge, M. A., I. Nimmo-Smith, A. M. Wing, and R. N. Totty. "The Dependence between Selected Categorical Measures of Cursive Handwriting." Journal of the Forensic Science Society 25, no. 3 (May 1985): 217–31. http://dx.doi.org/10.1016/s0015-7368(85)72394-8.

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Fellasufah, Fetty, and Ali Mustadi. "Cursive handwriting skills of primary school pre service teachers." Journal of Education and Learning (EduLearn) 13, no. 4 (November 1, 2019): 482. http://dx.doi.org/10.11591/edulearn.v13i4.13504.

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Wang, Jue, Chenyu Wu, Ying-Qing Xu, and Heung-Yeung Shum. "Combining shape and physical modelsfor online cursive handwriting synthesis." International Journal of Document Analysis and Recognition (IJDAR) 7, no. 4 (September 2005): 219–27. http://dx.doi.org/10.1007/s10032-004-0131-6.

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39

Bunke, H., M. Roth, and E. G. Schukat-Talamazzini. "Off-line cursive handwriting recognition using hidden markov models." Pattern Recognition 28, no. 9 (September 1995): 1399–413. http://dx.doi.org/10.1016/0031-3203(95)00013-p.

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40

Leng, Wong Yee, Siti Mariyam Shamsuddin, and Nor Azman Hashim. "Invariant behavioural based discrimination for individual representation." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 1 (February 1, 2021): 736. http://dx.doi.org/10.11591/ijece.v11i1.pp736-744.

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Writer identification based on cursive words is one of the extensive behavioural biometric that has involved many researchers to work in. Recently, its main idea is in forensic investigation and biometric analysis as such the handwriting style can be used as individual behavioural adaptation for authenticating an author. In this study, a novel approach of presenting cursive features of authors is presented. The invariants-based discriminability of the features is proposed by discretizing the moment features of each writer using biometric invariant discretization cutting point (BIDCP). BIDCP is introduced for features perseverance to obtain better individual representations and discriminations. Our experiments have revealed that by using the proposed method, the authorship identification based on cursive words is significantly increased with an average identification rate of 99.80%.
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Kutzner, Tobias, Carlos Pazmiño-Zapatier, Matthias Gebhard, Ingrid Bönninger, Wolf-Dietrich Plath, and Carlos Travieso. "Writer Identification Using Handwritten Cursive Texts and Single Character Words." Electronics 8, no. 4 (April 1, 2019): 391. http://dx.doi.org/10.3390/electronics8040391.

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One of the biometric methods in authentication systems is the writer verification/identification using password handwriting. The main objective of this paper is to present a robust writer verification system by using cursive texts as well as block letter words. To evaluate the system, two datasets have been used. One of them is called Secure Password DB 150, which is composed of 150 users with 18 samples of single character words per user. Another dataset is public and called IAM online handwriting database, and it is composed of 220 users of cursive text samples. Each sample has been defined by a set of features, composed of 67 geometrical, statistical, and temporal features. In order to get more discriminative information, two feature reduction methods have been applied, Fisher Score and Info Gain Attribute Evaluation. Finally, the classification system has been implemented by hold-out cross validation and k-folds cross validation strategies for three different classifiers, K-NN, Naïve Bayes and Bayes Net classifiers. Besides, it has been applied for verification and identification approaches. The best results of 95.38% correct classification are achieved by using the k-nearest neighbor classifier for single character DB. A feature reduction by Info Gain Attribute Evaluation improves the results for Naïve Bayes Classifier to 98.34% for IAM online handwriting DB. It is concluded that the set of features and its reduction are a strong selection for the based-password handwritten writer identification in comparison with the state-of-the-art.
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Hardcastle, R. A., and D. Kemmenoe. "A computer-based system for the classification of handwriting on cheques. Part 2: Cursive handwriting." Journal of the Forensic Science Society 30, no. 2 (March 1990): 97–103. http://dx.doi.org/10.1016/s0015-7368(90)73312-9.

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43

Ashok, M. Leonard, and Saumya. "PERCEPTION ON HANDWRITING AMONG SECONDARY SCHOOL STUDENTS." International Journal of Research -GRANTHAALAYAH 4, no. 5SE (May 31, 2016): 1–6. http://dx.doi.org/10.29121/granthaalayah.v4.i5se.2016.2707.

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Good handwriting serves as a primary tool of communication and knowledge assessment for students in classroom. Though computer and e- mail play an important role in our lives, nothing will ever replace the sincerity and individualism expressed through handwritten words. Our education system stress on student’s handwriting at primary level by introducing books like cursive writing, ruled notes and handwritten works. But as they upgrade, impact on handwriting slowly vanishes among students mainly at secondary level where they are stressed and deviated towards rote learning as a medium to fetch marks. But at this point we forget that only an aesthetic handwriting is a vehicle to impress and deliver our thoughts in the mind of evaluator, particularly when the evaluator is a stranger in case of public exams. This article is an attempt to find out the perception on handwriting among ninth standard school students and its influence on their academic achievement. This study has revealed that there is an impact of handwriting on academic achievement. Thereby it is stressed that handwriting should be given equal importance while teachers prepare their students for academic achievement.
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Wu Wei, and Gao Guanglai. "Online Cursive Handwriting Mongolia Words Recognition with Recurrent Neural Networks." International Journal of Information Processing and Management 2, no. 3 (July 31, 2011): 20–26. http://dx.doi.org/10.4156/ijipm.vol2.issue3.3.

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Bezine, Hala. "Authoring mobile tool for improving cursive handwriting learning/writing skills." International Journal of Learning Technology 15, no. 2 (2020): 150. http://dx.doi.org/10.1504/ijlt.2020.109572.

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Alimi, Adel M. "Evolutionary Computation for the Recognition of On-Line Cursive Handwriting." IETE Journal of Research 48, no. 5 (September 2002): 385–96. http://dx.doi.org/10.1080/03772063.2002.11416300.

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47

Wada, Yasuhiro, and Mitsuo Kawato. "A theory for cursive handwriting based on the minimization principle." Biological Cybernetics 73, no. 1 (July 1, 1995): 3–13. http://dx.doi.org/10.1007/s004220050158.

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Schomaker, Lambert, and Eliane Segers. "Finding features used in the human reading of cursive handwriting." International Journal on Document Analysis and Recognition 2, no. 1 (July 1, 1999): 13–18. http://dx.doi.org/10.1007/s100320050031.

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Morasso, P., L. Barberis, S. Pagliano, and D. Vergano. "Recognition experiments of cursive dynamic handwriting with self-organizing networks." Pattern Recognition 26, no. 3 (March 1993): 451–60. http://dx.doi.org/10.1016/0031-3203(93)90172-s.

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Wada, Yasuhiro, and Mitsuo Kawato. "A theory for cursive handwriting based on the minimization principle." Biological Cybernetics 73, no. 1 (June 1995): 3–13. http://dx.doi.org/10.1007/bf00199051.

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