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CHA, SUNG-HYUK, CHARLES C. TAPPERT, MICHAEL GIBBONS, and YI-MIN CHEE. "AUTOMATIC DETECTION OF HANDWRITING FORGERY USING A FRACTAL NUMBER ESTIMATE OF WRINKLINESS." International Journal of Pattern Recognition and Artificial Intelligence 18, no. 07 (2004): 1361–71. http://dx.doi.org/10.1142/s0218001404003642.
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We investigate the detection of handwriting forged by novices. To facilitate document examination it is important to develop an automated system to identify forgeries, or at least to identify those handwritings that are likely to be forged. Because forgers often carefully copy or trace genuine handwriting, we hypothesize that good forgeries — those that retain the shape and size of genuine writing — are usually written more slowly and are therefore wrinklier (less smooth) than genuine writing. From online handwriting samples we find that the writing speed of the good forgeries is significantly slower than that of the genuine writings. From corresponding offline samples we find that the wrinkliness of the good forgeries is significantly greater than that of the genuine writings, showing that this feature can help identify candidate forgeries from scanned documents. Using a total of eight handwriting distance features, including the wrinkliness feature, we train a neural network to achieved 89% accuracy on detecting forged handwriting on test samples from ten writers.
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Gupta, Shruti, Rakesh K. Garg, and Surinder Nath. "Assessment of natural variations in letter formations incorporating master pattern of handwriting." Nowa Kodyfikacja Prawa Karnego 44 (December 29, 2017): 65–85. http://dx.doi.org/10.19195/2084-5065.44.6.
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Handwriting is made up of countless habitual patterns. An individual’s handwriting is made up of a complexity of habitual patterns that are repeated within a typical range of variation around the model patterns. The muscle coordination of these and perceptions of how an individual sense form helps each one to develop one’s own Master Pattern of writing. A total number of 540 60 samples from each age group — 30 samples each from males & females, has been collected from 9 different age groups. Three handwriting samples — two in present handwriting with a gap of 5–10 minutes between the two handwritings and one old or past handwriting sample collected from each individual gap ranged from 2–33 years between present and old handwritings.With the due effect of the findings gathered from the present investigation the cases lacking in contemporary handwritings can be undertaken with ease by taking into account the letters — ‘f, y, r and i’ which have a major effect of natural variations on them which in turn makes them unreliable and should be avoided in the process of comparison as well as while forming an opinion.
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Coradinho, Helena, Filipe Melo, Gabriela Almeida, et al. "Relationship between Product and Process Characteristics of Handwriting Skills of Children in the Second Grade of Elementary School." Children 10, no. 3 (2023): 445. http://dx.doi.org/10.3390/children10030445.
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The purpose of this study was to analyze the relationship between the quality and speed of handwriting and the process characteristics of the handwriting of children in the second grade of elementary school considered as a function of age and gender. A sample of 57 children (mean age 7.25 years, SD 0.43) participated in the study. The Concise Assessment Method for Children’s Handwriting (BHK) was used to assess the quality and speed of handwriting. The characteristics of the process of handwriting were assessed using MovAlyzeR® software. The handwriting of boys showed a significantly greater number of strokes and slanted more to the right than the handwriting of girls. Handwriting quality and speed significantly correlated with several process characteristics: the number of strokes, reaction time, duration, relative pen-down duration, average pen pressure, vertical size, horizontal size, road length, and average absolute velocity. This research contributes to the construction of normative values in the process characteristics of the handwriting of elementary school children and provides a promising step towards the early identification of difficulties that can lead to dysgraphia, thus preventing later difficulties in handwriting.
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Dziechciaruk, Janusz. "Reflections on the nature of individuality in the process of identifying handwriting and signatures." Problems of Forensic Sciences, no. 125 (2021): 19–27. http://dx.doi.org/10.4467/12307483pfs.20.002.14782.
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The fundamental principle of identification, based on the condition that there is a unique combination of independent discriminating elements in handwriting, is analysed. The results of experiments conducted on distorted handwriting suggest that there may only be one handwriting habit that is responsible for the individuality of handwriting. By analysing disguised and simulated signatures and applying the isochrony principle, it seems reasonable to conclude that the mutual interaction between the speed of writing and the curvature of a graphic line is the only habit, and the quality of a graphic line the only feature responsible for the individuality of handwriting. The article introduces a number of ideas for evaluating the quality of a graphic line during handwriting examination, and a number of new opportunities for research into biometric electronic signatures.
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Lopez, C., A. Cannafarina, and L. Vaivre-Douret. "Validity of kinematics measures to assess handwriting development and disorders with a graphomotor task." European Psychiatry 64, S1 (2021): S212. http://dx.doi.org/10.1192/j.eurpsy.2021.564.
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IntroductionHandwriting disorder is considered to be one of the major public health problems among school-aged children worldwide. All the scales in the literature use handwriting tasks but it could be interesting to investigate a more accurate assessment of handwriting difficulties before the development and acquisition of handwriting as such.ObjectivesThe objective of our study is to examine the validity of a prescriptural task consisting of copying a line of cycloid loops in the diagnosis of handwriting disorders.Methods 35 children with handwriting disabilities and 331 typically developing right-handed children in primary school, aged 6-11 years old, were included in the study. They performed a copy of a line of cycloid loops, in an ecological setting, with a paper sheet put on the table. The kinematic measures were recorded with a digital pen. A Receiver Operating Characteristic method (ROC curve) was used to determine whether the loops line copy may be a sensitive test to diagnose handwriting disorders.Results Six kinematic variables recorded during the prescriptural task were found to be relevant markers of handwriting disorders with a sensibility between 0.743 and 0.880: strokes number, total and effective drawing time, in-air pauses times, loops number, number of peaks velocity.ConclusionsThe graphomotor task of copying a line of cycloid loops showed a good sensitivity to diagnose handwriting disorders and appeared to be a good predictor test, more particularly with the variables reflecting the strokes temporal organization.Drawing loops is a rapid graphomotor task, useful for exploring prerequisites of handwriting in screening for handwriting disorders.
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POTKONJAK, VELJKO. "ROBOTIC HANDWRITING." International Journal of Humanoid Robotics 02, no. 01 (2005): 105–24. http://dx.doi.org/10.1142/s021984360500034x.
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Handwriting has always been considered an important human task, and accordingly it has attracted the attention of researchers working in biomechanics, physiology, and related fields. There exist a number of studies on this area. This paper considers the human–machine analogy and relates robots with handwriting. The work is two-fold: it improves the knowledge in biomechanics of handwriting, and introduces some new concepts in robot control. The idea is to find the biomechanical principles humans apply when resolving kinematic redundancy, express the principles by means of appropriate mathematical models, and then implement them in robots. This is a step forward in the generation of human-like motion of robots. Two approaches to redundancy resolution are described: (i) "Distributed Positioning" (DP) which is based on a model to represent arm motion in the absence of fatigue, and (ii) the "Robot Fatigue" approach, where robot movements similar to the movements of a human arm under muscle fatigue are generated. Both approaches are applied to a redundant anthropomorphic robot arm performing handwriting. The simulation study includes the issues of legibility and inclination of handwriting. The results demonstrate the suitability and effectiveness of both approaches.
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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 device positions and identified the best interaction positions for various user postures. To flatten users' learning curves, we adapt our device to the established writing habits of different users by training a 36-character (i.e., A-Z, 0-9) recognition neural network in a human-in-the-loop manner. Such a personalization attempt ultimately achieves a low error rate of 0.005 on average for users with different writing styles. Subjective feedback shows that our solution has a good performance in system practicability and social acceptance. Empirically, we conducted a heuristic study to explore and identify the best interaction Position-Posture Correlation. Experimental results show that our Handwriting Velcro excels similar work [6] and commercial product in both practicality (12.3 WPM) and user-friendliness in different contexts.
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Furqat, Sobirov. "MODERN STATE OF FORENSIC HANDWRITING EXPERTISE." International Journal Of Law And Criminology 03, no. 01 (2023): 66–69. http://dx.doi.org/10.37547/ijlc/volume03issue01-12.
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This article presents a brief history of the emergence and development of forensic handwriting expertise, one of the independent types of traditional forensic examinations, the current state of forensic handwriting expertise in the Republic of Uzbekistan, its role in solving crimes, the modern relevance of this type of forensic examination. Also, in practice, a brief analysis of a number of problems existing in the process of forming the conclusions of the handwriting examination, which will be compiled at the end of this type of examination, was carried out, and proposals were made to eliminate these problems.
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Mamchur, O. "RESEARCH OF MANUSCRIPTS, PERFORMED ON DIFFERENT SURFACES AT CONDUCTING A FORENSIC HANDWRITING EXAMINATION." Criminalistics and Forensics, no. 66 (2021): 799–806. http://dx.doi.org/10.33994/kndise.2020.66.58.
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The purpose of the article is to study the issues of qualification of objects of forensic handwriting examination. Handwritten notes made on different surfaces in the system of objects of handwriting examination are considered. Specific features of the appointment and performance of handwriting examinations in relation to handwriting objects made on different surfaces are presented. It is concluded that the identification of the performer of a particular recording is possible only when the features of the performer’s handwriting are sufficiently reflected, which in its totality are individual, that is, inherent only in the handwriting of a certain person. As a result, individual strokes and numbers that do not reflect the totality of signs of the writing of their performers, as a rule, do not allow establishing the artist of these recordings. Despite a number of factors that make it difficult to study handwritten objects made on various surfaces, the solution of identification and some diagnostic problems for this type of objects is still possible. This possibility is determined not only by the output characteristics of the handwritten note being studied, but also by the preparation for conducting the corresponding research at all stages of the appointment and carrying out forensic handwriting examination.
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UEDA, KATSUHIKO, KEN'ICHI MATSUO, and BONNIE L. SCHWID. "EXPERIMENTAL ANALYSIS OF INDIVIDUALITY OF JAPANESE HANDWRITING." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 05 (2009): 869–85. http://dx.doi.org/10.1142/s0218001409007545.
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A handwriting examination to determine the writer (writer verification and writer identification) is based upon the hypothesis that each person has characteristic handwriting that is distinct from the handwriting of other individuals. This hypothesis has been accepted empirically through many case studies. However, this hypothesis must be established with scientific and quantitative rigor in order to increase its admissibility as forensic evidence. This paper describes the results of a quantitative investigation into individuality of Japanese handwriting. The authors investigated the individuality (consistency and discriminability) of normal Japanese handwriting using variation entropy and writer verification algorithms. We also compared these results with the analysis results of Japanese signatures that we investigated previously. The authors discuss the differences of individuality between normal handwriting and signatures. Individuality of normal handwriting becomes noticeable as the number of strokes increases, and varies with the complexity of the character class. Although individuality of both normal handwriting and signatures also depends on the writer, this is more noticeable for signatures. It is important that these facts are considered in actual forensic analysis of Japanese handwriting.
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Delazer, Margarete, Laura Zamarian, and Atbin Djamshidian. "Handwriting in Alzheimer’s Disease." Journal of Alzheimer's Disease 82, no. 2 (2021): 727–35. http://dx.doi.org/10.3233/jad-210279.
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Background: Agraphia is a typical feature in the clinical course of Alzheimer’s disease (AD). Objective: Assess the differences between AD and normal aging as regards kinematographic features of handwriting and elucidate writing deficits in AD. Methods: The study included 23 patients with AD (78.09 years/SD = 7.12; MMSE 21.39/SD = 3.61) and 34 healthy controls (75.56 years/SD = 5.85; MMSE 29.06/SD = 0.78). Both groups performed alphabetical and non-alphabetical writing tasks. The kinematographic assessment included the average number of inversions per stroke (NIV; number of peaks in the velocity profile in a single up or down stroke), percentage of automated segments, frequency (average number of strokes per second), writing pressure, and writing velocity on paper. Results: A total of 14 patients showed overt writing difficulties reflected by omissions or substitutions of letters. AD patients showed less automated movements (as measured by NIV), lower writing velocity, and lower frequency of up-and-down strokes in non-alphabetical as well as in alphabetical writing. In the patient group, Spearman correlation analysis between overt writing performance and NIV was significant. That means patients who had less errors in writing a sentence showed a higher automaticity in handwriting. The correctness of alphabetical writing and some kinematographic measures in writing non-alphabetical material reached excellent diagnostic values in ROC analyses. There was no difference in the application of pressure on the pen between patients and controls. Conclusion: Writing disorders are multi-componential in AD and not strictly limited to one processing level. The slow and poorly automated execution of motor programs is not bound to alphabetical material.
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Tamošiūnaitė, Rasa. "HANDWRITING EXPERTISE IN THE FORENSIC SCIENCE CENTRE OF LITHUANIA: PRESENT ACTIONS AND FUTURE VISION." Criminalistics and Forensics, no. 67 (August 9, 2022): 365–71. http://dx.doi.org/10.33994/kndise.2022.67.37.
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Recently, the number of expert investi gations in handwriting evidence at the Lithuanian Forensic Examination Centre (LTEC) has significantly decreased. The decrease has been observed for several years already. The present article analyses possible reasons for the decrease in the number of expert investigations in handwriting in LT EC and the importance of such investigation in the context of judicial research, court proceeding, and criminal investigations. Also, the article deals with the use of electronic signatures and associated security problems in the digital environment. Key words: handwriting examination, signature, digital signature, digitized sig- nature, informativeness of signatures.
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Pathak, Ajeet Ram, Abhishek Raut, Soham Pawar, Mansi Nangare, Harshdeep Singh Abbott, and Pawan Chandak. "Personality analysis through handwriting recognition." Journal of Discrete Mathematical Sciences and Cryptography 23, no. 1 (2020): 19–33. http://dx.doi.org/10.1080/09720529.2020.1721856.
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Birincioğlu, İsmail, Mustafa Uzun, Nevzat Alkan, Ömer Kurtaş, Rıza Yılmaz, and Muhammet Can. "Changes in Handwriting due to Alzheimer’s Disease a Case Report." Bulletin of Legal Medicine 21, no. 2 (2016): 116–25. http://dx.doi.org/10.17986/blm.2015220397.
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Handwriting and signature comparisons are frequently performed in forensic investigations of documents. Mistakes in conclusion might be due to lack of sufficient documentation and information. Many factors can affect handwriting and signature. These factors are divided into two groups: dependent or independent of the individual at the time the handwriting or signature is made. Therefore, the situations leading to differences between individuals and in the individual’s own handwriting and signature should be determined. Currently, average life expectancy and quality have increased due to the developments in health services. Thus, an increasing number of elderly people are engaged in an active daily life and trade. Alzheimer’s Disease (AD) can develop in the elderly; this is a condition that could alter handwriting and signature considerably over time.In forensic document examination, comparing the document in question containing handwriting or signature with the original documents prepared before the document in question was prepared is important. However, if alterations have developed secondary to a disease, the documents prepared before and after the disease affected the individual should be assessed together.Likewise, in the present case, the examiners making comparisons using handwriting and signatures from different periods reached entirely different conclusions.The case is a bill prepared in 1994. The payee of the bill is a male born in 1925 and diagnosed with AD shortly before his death in 1998. The indebted person in the bill is the payee’s spouse. For the assessment of handwriting and signature, the first endorsement consisting of the handwritten name and signature was used. Several expert reports regarding the same document had been commissioned; these reached different views. The document was sent to The Council of Forensic Medicine to assess the identification and the previous reports. After re-evaluation, the handwriting was declared to belong to the payee.In this study, the reason that the experts delivered opposing opinions on the identification of a document handwritten by a person with AD after being referred to court was investigated.Based on the judicial file, the document examiners did not have the opportunity to evaluate all factors affecting the handwriting or signature of the individual, and an adequate number of handwriting and signature examples were not collected for comparison. Consequently, the examiners reached differing opinions. Thus, the above-mentioned factors are necessary to derive a satisfactory and accurate opinion regarding the identification of handwriting or signature.Keywords: Forensic sciences, handwriting, signature, Alzheimer’s Disease.
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Gawda, Barbara. "Lack of evidence for the assessment of personality traits using handwriting analysis." Polish Psychological Bulletin 45, no. 1 (2014): 73–79. http://dx.doi.org/10.2478/ppb-2014-0011.
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Abstract Background: The aim of the study is to clarify the beliefs and numerous doubts about the possible identification of personality in handwriting. The purpose of the described studies was to show an association between personality traits and handwriting features. The author aimed to verify whether or not there are any specific characteristics of writing in relation to personality traits. | Method: Two different studies have been described. A different number of subjects participated in the investigations and different techniques for personality assessment were used; however, the same set of handwriting features was analyzed in each study. In the first study the NEO-FFI (n=260) was used, while the EPQ-R (n=180) was used in the second study. The graphic characteristics of the handwriting samples were examined by forensic experts. | Results: The correlations between the personality traits and handwriting features were counted. The results showed that there were few significant correlations between handwriting parameters and personality traits, as measured by the NEO-FFI and EPQ-R. | Conclusions: No writing characteristics were specific to personality traits. There is no evidence for assessment of personality on the base of handwriting
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Masasi, Gianino, James Purnama, and Maulahikmah Galinium. "Development of an on-Premise Indonesian Handwriting Recognition Backend System Using Open Source Deep Learning Solution For Mobile User." Journal of Applied Information, Communication and Technology 7, no. 2 (2021): 91–97. http://dx.doi.org/10.33555/jaict.v7i2.109.
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Existing handwriting recognition solution on mobile app provides off premise service which means the handwriting is processed in overseas servers. Data sent to abroad servers are not under our control and could be possibly mishandled or misused. As recognizing handwriting is a complex problem, deep learning is needed. This research has the objective of developing an on premise Indonesian handwriting recognition using open source deep learning solution. Comparison of various deep learning solution to be used in the development are done. The deep learning solution will be used to build architectures. Various database format are also compared to decide which format is suitable to gather Indonesian handwriting database. The gathered Indonesian handwriting database and built architectures are used for experiments which consists of number of Convolutional Neural Network (CNN) layers, rotation and noise data augmentation, and Gated Recurrent Unit (GRU) vs Long Short Term Memory (LSTM). Experiment results shows that rotation data augmentation is the parameter to be change to improve word accuracy and Character Error Rate (CER). The improvement is 64.8% and 23.2% to 69.6% and 20.6% respectively.
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Brіukhan, S. F., and N. V. Sirotenko. "PECULIARITIES OF PERFORMING MULTIOBJECT FORENSIC HANDWRITING EXAMINATIONS." Theory and Practice of Forensic Science and Criminalistics 18 (December 26, 2018): 295–300. http://dx.doi.org/10.32353/khrife.2018.33.
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The specifics of multiobject forensic handwriting examination is considered. Urgency of their performing and the importance for making decisions on the case are underlined. Attention is drawn to the solution of specific problems that require different approaches, depending on the nature of research objects. Multistage character of research on a multitude of handwriting objects is singled out and features of a separate stage, namely preliminary research are considered. Importance of this stage for the further research planning, selection of the corresponding separate techniques by expert, construction of expert versions is revealed. It is pointed out that methods of performing multiobject handwriting examination at the stage of preliminary research has features that distinguish it from other handwriting methods. Issues related to familiarization with the submitted materials, verification of comparability of samples and establishment of comparative material sufficiency are touched upon. Importance of resolving the issue regarding the need to send an application by a expert is noted. Also, the completion of the preliminary research stage by the formulation of general and private versions per se of the tasks assigned to expert is noted. Recently expert practice requires from experts not only to update methodological approaches in solving handwriting tasks when granting a large number of objects, but also to improve methods of performing relevant research. Above mentioned sequence of research stages can be one of the types of guarantee for full and comprehensive objective research on a large number of objects, an serve the basis for a correct expert decision. Consideration of the issues of features on the stage of preliminary research in carrying out a multiobject handwriting examination is important for handwriting experts, both beginners and with great experience.
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Lambert, Eric, Sonia Kandel, Michel Fayol, and Eric Espéret. "The effect of the number of syllables on handwriting production." Reading and Writing 21, no. 9 (2007): 859–83. http://dx.doi.org/10.1007/s11145-007-9095-5.
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Putri, Farica Perdana, and Adhi Kusnadi. "Pengenalan Tulisan Tangan Offline Dengan Algoritma Generalized Hough Transform dan Backpropagation." Jurnal ULTIMA Computing 10, no. 1 (2018): 5–12. http://dx.doi.org/10.31937/sk.v10i1.890.
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Offline handwriting recognition is a technique used to recognize handwriting in paper document which converting it to digital form. Each handwriting has a unique style and shape that can be used to identify the owner. This research aims to develop a method to recognize the digital data handwriting. The method combines two algorithms; the first is Generalized Hough Transform in feature extraction process to detect arbitrary objects on the image; the second algorithm is Backpropagation to train the neural network based on feature values from feature extraction process. Artificial Neural Network (ANN) is used to improve the accuracy of the recognition system. The experiments are performed by using 100 handwriting images of 10 different people. The number of hidden units is defined through experiment to obtain optimal neural network. The experiment result shows that the recognition accuracy is up to 80%.
 Index Terms—Artificial Neural Network, Backrpopagation, Generalized Hough Transform, Offline handwiritng recognition
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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 (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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M. Butaev, Mikhail, Mikhail Yu. Babich, Igor I. Salnikovq, Alexey I. Martyshkin, Dmitry V. Pashchenko, and Dmitry A. Trokoz. "Neural Network for Handwriting Recognition." Nexo Revista Científica 33, no. 02 (2020): 623–37. http://dx.doi.org/10.5377/nexo.v33i02.10798.
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Today, in the digital age, the problem of pattern recognition is very relevant. In particular, the task of text recognition is important in banking, for the automatic reading of documents and their control; in video control systems, for example, to identify the license plate of a car that violated traffic rules; in security systems, for example, to check banknotes at an ATM and in many other areas. A large number of methods are known for solving the problem of pattern recognition, but the main advantage of neural networks over other methods is their learning ability. It is this feature that makes neural networks attractive to study. The article proposes a basic neural network model. The main algorithms are considered and a programming model is implemented in the Python programming language. In the course of research, the following shortcomings of the basic model were revealed: low learning rate (the number of correctly recognized digits in the first epochs of learning); retraining - the network has not learned to generalize the knowledge gained; low probability of recognition - 95.13%.To solve the above disadvantages, various techniques were used that increase the accuracy and speed of work, as well as reduce the effect of network retraining.
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Zolota, L., and I. Savchenko. "Problems of appointment of handwritten examination in civil judiciary." Uzhhorod National University Herald. Series: Law, no. 70 (June 18, 2022): 175–80. http://dx.doi.org/10.24144/2307-3322.2022.70.25.
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The article is devoted to the issue of this type of evidence as providing an expert opinion in civil proceedings on the example of handwriting examination, because it is one of the most common types of forensic examination. It is known that the objects of handwriting are all possible types of documents, where the text is handwritten or signed by a person. The article deals with the tasks of forensic handwriting examination and their purpose. The current problems of appointment of handwriting examination by the court, which arise, in particular, when taking handwriting samples, are considered. The sources from which the expert obtains information to provide an opinion are analyzed, as it is not only samples of handwriting or signature, but also information about the person to be identified. It is considered how the court determines the scope of examination, formulates questions and collects free samples of handwriting and signatures, which are compared, as well as selects experimental samples. There is a ban on forensic experts who are employees of government agencies to conduct handwriting examinations on copies of documents. The article argues that it is possible to conduct handwriting examination of copies of documents, especially if it is physically impossible to provide the original document, provided that the court will take this fact into account. Since the expert does not have the right to collect materials for the examination, the expert is provided by the court or the party to the case. It is investigated that the legislation does not clearly regulate who exactly and how should take experimental samples of handwriting or signature, as it is prescribed in the scientific and methodological recommendations for the preparation and appointment of forensic examinations and expert research. In this regard, it is proposed to recommend that courts involve a handwriting specialist in the procedure for taking a sufficient number of experimental samples for examination.
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CORDELLA, L. P., C. DE STEFANO, and F. FONTANELLA. "EVOLUTIONARY PROTOTYPING FOR HANDWRITING RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 01 (2007): 157–78. http://dx.doi.org/10.1142/s0218001407005351.
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A new prototyping method based on the evolutionary computation paradigm and on the concept of Vector Quantization is proposed. It uses a specifically devised evolutionary algorithm for evolving a set of prototype feature vectors and does not require any a priori knowledge about either the actual number of prototypes or the statistical properties of the input data. Experiments performed by using both synthetic data and handwritten digits randomly extracted from the NIST database have confirmed the effectiveness of the approach.
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I. Abdalla, Mahmoud, Mohsen A. Rashwan, and Mohamed A. Elserafy. "Generating realistic Arabic handwriting dataset." International Journal of Engineering & Technology 8, no. 4 (2019): 460. http://dx.doi.org/10.14419/ijet.v8i4.29786.
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During the previous year's holistic approach showing satisfactory results to solve the problem of Arabic handwriting word recognition instead of word letters segmentation. In this paper, we present an efficient system for generation realistic Arabic handwriting dataset from ASCII input text. We carefully selected simple word list that contains most Arabic letters normal and ligature connection cases. To improve the performance of new letters reproduction we developed our normalization method that adapt its clustering action according to created Arabic letters families. We enhanced Gaussian Mixture Model process to learn letters template by detecting the number and position of Gaussian component by implementing Ramer-Douglas-Peucker algorithm which improve the new letters shapes reproduced by using and Gaussian Mixture Regression. We learn the translation distance between word-part to achieve real handwriting word generation shape. Using combination of LSTM and CTC layer as a recognizer to validate the efficiency of our approach in generating new realistic Arabic handwriting words inherit user handwriting style as shown by the experimental results.
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Golubović, Slavica, Nevena Ječmenica, Marijana Panić, and Vladica Žikić. "The characteristics of reading and orthography in children with dysgraphic handwriting." Nastava i vaspitanje 69, no. 2 (2020): 213–37. http://dx.doi.org/10.5937/nasvas2002213g.
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The aim of this study was to analyze the characteristics of reading and the orthographic level of handwriting in children with dysgraphic handwriting and children with typical development in the early grades of primary school. The research sample consisted of 94 children in the 3rd and 4th grades of primary school. The Handwriting Dysgraphia Assessment Test was used to assess the level of handwriting dysgraphia, while the analysis of the orhographic level of handwriting involved the assessment of the frequency and type of orthographic errors. The Three-Dimensional Reading Test was used in the second stage of research. Findings show the overall prevalence of handwriting dysgraphia in 13.8% of children, and a greater prevalence in boys (69.1%) relative to girls (30.9%). In the group of children with dysgraphic handwriting, 53.8% were categorized as having "graphomotor dysgraphia", while 46.2% were categorized as having "language dysgraphia". The study results suggest that there are no statistically significant differences between 3rd and 4th grade students in the application of orthographic rules to all writing tasks (t=1,33; df=11; p=0,17). Children with dysgraphic handwriting achieved poorer results on all writing tasks relative to children with typical development. A comparison of results of children with dysgraphic handwriting and children with typical development did not reveal statistically significant differences in reading speed tasks (t=1,10; df=18; p=0,32), number of mistakes (t=0,73; df=21; p=0,46), and reading comprehension (t=0,66; df=14; p=0,51). Increasing awareness of aspects of writing and reading where difficulties may occur, but also of aspects that represent children's developmental strengths, along with an effective assessment of language and cognitive abilities, would significantly improve the quality of the teaching process.
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Alamargot, Denis, Marie-France Morin, and Erika Simard-Dupuis. "Handwriting Delay in Dyslexia: Children at the End of Primary School Still Make Numerous Short Pauses When Producing Letters." Journal of Learning Disabilities 53, no. 3 (2020): 163–75. http://dx.doi.org/10.1177/0022219420903705.
Texte intégralRésumé :
Developmental dyslexia is defined as a specific reading disorder but is also thought to be underpinned by a deficit in motor skills that may well affect handwriting performance. However, the results of studies addressing this issue are not consistent. The present study was, therefore, designed to better understand the functioning of handwriting in children with dyslexia, by conducting an analysis of the legibility and fluency of handwritten letters, supplemented by an assessment of motor skills. The performances of 15 children with dyslexia ( Mage = 11.4 years) were compared with those of two groups of typically developing children, one matched for chronological age, the other for orthographic level ( Mage = 8.7 years) on two handwriting measures (production of the letters of the alphabet and the child’s first name and surname). Results revealed a delay in motor skills, as well as in letter legibility, letter production duration, and the number of short pauses (i.e., lasting between 20 and 199 ms) made during letter production, in the children with dyslexia, with strong negative correlations between motor skills and the number of short pauses. Results are discussed in the context of handwriting control development in children, and perspectives are set out for practitioners.
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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 (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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Zhizhina, M. V., and I. R. Yagut’yan. "On the Form and Content of a Forensic Handwriting Expert’s Opinion." Theory and Practice of Forensic Science 14, no. 2 (2019): 51–60. http://dx.doi.org/10.30764/1819-2785-2019-14-2-51-60.
Texte intégralRésumé :
The content of a forensic handwriting expert’s opinion has been holding attention of both research scientists and practicing experts for many decades now since it is not only an essential evidence in court often being crucial to deciding on a case but also a document bringing together procedural and scientific methodological elements. Forensic handwriting examination is much in demand for court, it is also profoundly and comprehensively developed, supported by detailed methodological recommendations on drafting and processing of an expert’s opinion. However, the analysis of experts’ opinions from state forensic institutions as well as from non-state brought to light several shortcomings which do not enhance their evidentiary status. This applies in particular to performing the forensic examination by non-state experts who tend to lack competence. At the same time the number of forensic handwriting examinations assigned by court to the non-state forensic institutions prevails significantly.The most common mistakes made by the handwriting experts when drafting opinions are reviewed in the article, some ways to resolve them are proposed. The need to provide methodological recommendations for forensic handwriting experts incorporating current requirements to the experts’ opinions is acknowledged.
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Jeiad, H. A. "Indian Number Handwriting Features Extraction and Classification using Multi-Class SVM." Engineering and Technology Journal 36, no. 1A (2018): 33–40. http://dx.doi.org/10.30684/etj.2018.136753.
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Tsao, Raphaele, Eloise Moy, Jean-Luc Velay, Nicolas Carvalho, and Carole Tardif. "Handwriting in Children and Adults With Down Syndrome: Developmental Delay or Specific Features?" American Journal on Intellectual and Developmental Disabilities 122, no. 4 (2017): 342–53. http://dx.doi.org/10.1352/1944-7558-122.4.342.
Texte intégralRésumé :
Abstract While there is a long history and tradition of behavioral research on basic motor skills in Down syndrome (DS), there has been only limited research on handwriting ability. We analyzed the spatiotemporal features of handwriting produced by children and adults with DS (n = 24), and compared their productions with those of comparison groups matched for developmental (n = 24) or chronological (n = 24) age. Results indicated that the participants with DS performed an alphabet letter-writing task just as efficiently as the children of the same developmental age, in terms of the length, duration and speed of their handwriting, and the number and duration of their pauses. Our study highlights a substantial delay in the stages of writing acquisition.
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Kumalasanti, Rosalia Arum. "Writer Identification Based on Hand Writing using Artificial Neural Network." International Journal of Applied Sciences and Smart Technologies 3, no. 2 (2021): 257–64. http://dx.doi.org/10.24071/ijasst.v3i2.3920.
Texte intégralRésumé :
Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.
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Pasta, Gianluigi, Maria Elisa Mancuso, Filomena De Felice, et al. "Handwriting Analysis in Children and Adolescents with Hemophilia: A Pilot Study." Journal of Clinical Medicine 9, no. 11 (2020): 3663. http://dx.doi.org/10.3390/jcm9113663.
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Background: Handwriting is a complex task that requires the integrity of different sensorimotor components to be performed successfully. Patients with hemophilia suffer from recurrent joint bleeds that may occur in the elbow, causing elbow dysfunction with handwriting performance impairment. In our study, we described instrumental dysgraphia that is related to functional disturbances. This pilot study aims to evaluate the handwriting performance in a group of patients with hemophilia. Methods: The study was performed at the Angelo Bianchi Bonomi Hemophilia and Thrombosis Center in Milan. Boys with severe and moderate hemophilia A and B regularly followed-up at that Center, with age between 6–19 years, were eligible. Patients were invited to the Center for one multidisciplinary evaluation of the upper limbs that included: Clinical examination, surface electromyography, and handwriting assessment. Results: All patients, but one, completed handwriting assessment. Overall, 14/19 (74%) had abnormal handwriting, which was overt instrumental dysgraphia in six (32%). There was no difference in Hemophilia Joint Health Score (HJHS) between dysgraphic and non-dysgraphic boys, while surface electromyography (sEMG) revealed a prevalence of flexor muscles of the upper limb in dysgraphic as compared with non-dysgraphic boys. Conclusions: The rather high prevalence of instrumental dysgraphia found in this pilot study deserves a further development of this preliminary experience by increasing the number of examined patients and comparing them with a control group, including quality of life and psychological assessment.
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Wang, Weilan, Zhengjiang Li, Zhengqi Cai, Xiaobao Lv, Caike Zhaxi, and Yuehui Han. "Online Tibetan Handwriting Recognition for Large Character Set on New Databases." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 10 (2019): 1953003. http://dx.doi.org/10.1142/s0218001419530033.
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The online handwriting recognition of Tibetan characters is still in its infancy. For further research, an online handwriting database of large Tibetan character set was developed, and a recognition research was carried out on this database as a baseline result. The Northwest Minzu University Online Tibetan Handwriting Database (NMU-OLTHWDB) contains 7240 different types of characters, and the sample number in each type is 5000. The total number of samples is [Formula: see text]. The database covers Tibetan Character Collection, Information Technology Tibetan Coded Character set (Extension Set A), and Information Technology Tibetan Coded Character set (Extension Set B). The characters in the database are composed of 170 types of different components. We studied the online handwritten Tibetan recognition software also, and the character feature extraction, classifier training, and the statistics and analysis of the recognition results on the test set were mainly introduced. The character features included the direction attribute coefficients and spatial combination, and the feature matrix was compressed by Linear Discriminate Analysis (LDA). A quick classifier was designed by a modified quadratic discriminate function (QMQDF), and was trained with 4500 sets of samples. In the large character set, the recognition rates of top 1, top 3, top 5, and top 10 were 75.2%, 89.56%, 93.02%, and 95.96%, respectively. Moreover, an online handwriting recognition system for Tibetan large character set was designed with good performance.
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Millstone, Noah. "Special Forum: Handwriting and Power in Early Stuart England Introduction." Journal of British Studies 62, no. 1 (2023): 132–33. http://dx.doi.org/10.1017/jbr.2023.1.
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Early Stuart England was awash in handwriting. Handwriting was the medium of property records, law, account books, and scholarly note taking. A large share of government was conducted through handwritten policy briefs, registers, and circular letters. Equally, it was the medium of prisoners, beggars, petitioners, and village wits. Collectors compiled handwritten poems, prophecies, speeches, recipes, and anecdotes. The number of English people who knew how to operate a printing press was probably in the low hundreds, the number who could write at least a bit likely in the hundreds of thousands. Writing was accessible, widely understood, and practiced. It was the medium to hand.
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Sohranych, T. V., N. V. Syrotenko-Katsman, N. Y. Didushok, and V. G. Abrosymova. "RELEVANT ISSUES OF STUDYING SIGNATURES MADE BY PEOPLE WITH DELIBERATE CHANGE OF THEIR REGULAR SIGNATURE HANDWRITING FEATURES." Theory and Practice of Forensic Science and Criminalistics 15 (November 30, 2016): 229–37. http://dx.doi.org/10.32353/khrife.2015.26.
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Signatures, given their specific nature and functional purpose, represent the most common object for the study of handwriting. The expert practice shows that among true signatures, i. e. signatures made by respective signatories, there are often signatures that have been deliberately changed by the signatory to make it look as a fake signature. In order to find a proper solution to problems regarding the given objects, it is necessary to know the ways of changing the signature handwriting by the signatory, understand their peculiarities, have considerable array of comparative material for a long period of time and represented in the form of not only samples of signatures made by persons in question, but the samples of their handwriting as well. The article provides a number of types of changing the features of signature handwriting and provides some examples from practice. Based on the undertaken research the obtained data on the processes of changing the signatures by signatories themselves allow to create an expert methodology to be used in especially challenging signature study tasks.
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Karapetyan, M. Zh. "MAIN METHODICAL PROVISIONS AND FEATURES OF PERFORMING REPEATED HANDWRITING EXAMINATIONS IN THE REPUBLIC OF ARMENIA." Theory and Practice of Forensic Science and Criminalistics 17 (November 29, 2017): 205–12. http://dx.doi.org/10.32353/khrife.2017.26.
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The paper is devoted to the general technique of research when performing repeated handwriting examinations in the Republic of Armenia which has the features inherent to all stages of expert research. It’s underlined that at the preparatory stage of research the experts, to whom the performance of a repeated examination is entrusted, should clarify a number of the circumstances important only for the repeated researches. Importance of repeated handwriting examination for the purpose of excluding inspector and court doubts concerning the primary conclusions and explanation of the reasons for discrepancies in the experts’ conclusions is noted. The paper pays special attention that in the deep and qualified analysis of an expert conclusion who performed a primary examination and in the case of discrepancy of the primary and repeated examination conclusions, the inspector and court are given with the possibility to estimate correctly both conclusions. It’s underlined that a primary goal of a handwriting expert at the stage of separate research of an object consists in revealing separate, most significant signs inherent for a definite handwriting and identification totality of signs which would individualize handwriting of a certain person. The expert evaluates the handwriting signs at all stages of the conducted research, but evaluation acquires the greatest significance after carrying out comparative research when a certain amount of coincident and/or differing signs is already revealed and the expert should define their identification importance and sufficiency for a conclusion. The attention is paid that materiality of a sign, though being an objective category, but subjective factor at identification research plays of no small important role and in the case when the expert does not possess profound knowledge in the field of forensic handwriting, sufficient experience, accuracy of visual perceptions he may come to incorrect conclusions.
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Supriadi, Supriadi. "Simple Handwriting Calculator Application Using Optical Character Recognition (OCR)." Buletin Ilmiah Sarjana Teknik Elektro 2, no. 3 (2021): 163. http://dx.doi.org/10.12928/biste.v2i3.3348.
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The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform calculation operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user will see the written text and remember it again so that it takes longer to perform calculations using a calculator. The method used in this study is Optical Character Recognition, this method can recognize text contained in images or handwritten images of mathematical number operations. The results of the introduction of the text will then be carried out by arithmetic calculations to get the calculation results. From the trials on 20 handwritten images of mathematical number operations, the results obtained were 85% accuracy of extraction and accuracy of handwritten images that can be calculated and correct by 85%.
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Supriadi, Supriadi. "Simple Handwriting Calculator Application Using Optical Character Recognition (OCR)." Buletin Ilmiah Sarjana Teknik Elektro 2, no. 3 (2021): 163. http://dx.doi.org/10.12928/biste.v2i3.3348.
Texte intégralRésumé :
The calculator is a calculation tool that is widely used in various specialized fields of business and commerce. The use of a calculator makes it easier for humans to perform calculation operations, but there are obstacles in the process of inputting numbers if you want to calculate the value of numbers on written media such as paper, whiteboards and so on. The user must first see the text on written media, then read it and remember it then type the writing on a calculator tool or application. The drawback of this method is that when the user forgets the writing on the written media, the user will see the written text and remember it again so that it takes longer to perform calculations using a calculator. The method used in this study is Optical Character Recognition, this method can recognize text contained in images or handwritten images of mathematical number operations. The results of the introduction of the text will then be carried out by arithmetic calculations to get the calculation results. From the trials on 20 handwritten images of mathematical number operations, the results obtained were 85% accuracy of extraction and accuracy of handwritten images that can be calculated and correct by 85%.
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M. Joshi, Aditi, and Sanjay G. Patel. "An Ancient Number Recognition using Freeman Chain Code with Deep Learning Approach." Computer Science & Engineering: An International Journal 12, no. 1 (2022): 9–18. http://dx.doi.org/10.5121/cseij.2022.12102.
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Sanskrit character and number documents have a lot of errors. Correcting those errors using conventional spell-checking approaches breaks down due to the limited vocabulary. This is because of high inflexions of Sanskrit, where words are dynamically formed by Sandhi rules, Samasa rules, Taddhita affixes, etc. Therefore, correcting OCR documents require huge efforts. Here, we can present different machine learning approaches and various ways to improve features for ameliorating the error corrections in Sanskrit documents. Simulation of Sanskrit dictionary for synthesizing off-the-shelf dictionary can be done. Most of the proposed methods can also work for general Sanskrit word corrections and Hindi word corrections. Handwriting recognition in Indic scripts, like Devanagari, is very challenging due to the subtitles in the scripts, variations in rendering and the cursive nature of the handwriting. Lack of public handwriting datasets in Indic scripts has long stymied the development of offline handwritten word recognizers and made comparison across different methods a tedious task in the field. In this paper, a new handwritten word dataset will be released for Devanagari, IIIT-HW-Dev to alleviate some of these issues. This process is required for successful training of deep learning architecture, availability of huge amounts of training data is crucial, as any typical architecture contains millions of parameters. A new method for the classification of freeman chain code using four-connectivity and eight-connectivity events with deep learning approach is presented. Application of CNN LeNet-5 is found to be suitable to get results in this cases as the numbers are formed with curved lines In contrast with the existing FCC event data analysis techniques, sampled grey images of the existing events are not used, but image files of the three-phase PQ event data are analysed by taking the advantage of the success of the deep learning approach on imagefile-classification. Therefore, the novelty of the proposed approach is that image files of the voltage waveforms of the three phases of the power grid are classified. It is shown that the test data can be classified with 100% accuracy. The proposed work is believed to serve the needs of the future smart grid applications, which are fast and taking automatic countermeasures against potential PQ events.
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Michalik, Bernadetta, and Marek Miłosz. "Neural networks in recognition of handwriting." Journal of Computer Sciences Institute 15 (June 30, 2020): 109–13. http://dx.doi.org/10.35784/jcsi.2037.
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Artificial neural networks consist of many simple elements capable of processing data. These are tools inspired by the construction of the human brain, used in machine learning. The aim of the research was to analyze the occuracy of the created neural network in the process of handwriting recognition. The article presents the results obtained during the learning and testing of a convolution network with a different number of hidden layers. Each time learning and testing the network was carried out using the same set of images (taken from the publicly available IAM database) depicting handwritten words in English.
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Kučerová, Olga, and Anna Kucharská. "Acquiring penmanship and writing skills from the first to fifth grade of primary school: Joined-up writing vs. Comenia Script." Journal of Language and Cultural Education 6, no. 2 (2018): 1–13. http://dx.doi.org/10.2478/jolace-2018-0012.
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Abstract The project presented here deals with a typical human means of communication – writing. The aim of the project is to map the developmental dynamics of handwriting from the first to the fifth grade of primary school. The question remains topical because of the fact that several systems of writing have been used in the past few years. Our project focuses on comparing the systems of joined-up handwriting (the standard Latin alphabet) and the most widespread form of printed handwriting: Comenia Script. The research can be marked as sectional; pupils took a writing exam at the beginning and at the end of the 2015/2016 school year. The total number of respondents was 624 pupils, evenly distributed according to the school year, system of writing and gender. To evaluate handwriting, the evaluation scale of Veverková and Kucharská (2012) was adjusted to include a description of phenomena related to graphomotor and grammatical aspects of writing, including the overall error rate and work with errors. Each area that was observed included a series of indicators through which it was possible to create a comprehensive image of the form handwriting took in the given period. Each indicator was independently classified on a three-point scale. Thanks to that, a comprehensive image of the form of writing of a contemporary pupil emerged.
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YASUDA, H., K. TAKAHASHI, and T. MATSUMOTO. "A DISCRETE HMM FOR ONLINE HANDWRITING RECOGNITION." International Journal of Pattern Recognition and Artificial Intelligence 14, no. 05 (2000): 675–88. http://dx.doi.org/10.1142/s021800140000043x.
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A fast HMM algorithm is proposed for online handwritten character recognition. After preprocessing the input strokes are discretized so that a discrete HMM is used. This particular discretization naturally leads to a simple procedure for assigning the initial state and state transition probabilities. In the training phase, complete marginelization with respect to state is not performed. A criterion based on normalized maximum likelihood ratio is given for deciding when to create a new model for the same character in the learning phase, in order to cope with stroke order variations and large shape variations. Experiments are done on the Kuchibue data base from Tokyo University of Agriculture and Technology. The algorithm appears to be very robust against stroke number variations and have reasonable robustness against stroke order variations and large shape variations. A drawback of the proposed algorithm is its memory requirement when the number of character classes and their associated models becomes large. Density tying is discussed in order to overcome this problem.
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Vaughn, Sharon, Jeanne Shay Schumm, and Jane Gordon. "Early Spelling Acquisition: Does Writing Really Beat the Computer?" Learning Disability Quarterly 15, no. 3 (1992): 223–28. http://dx.doi.org/10.2307/1510245.
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This research evaluated the efficacy of handwriting, letter tiles, and computer-based instruction on the early spelling acquisition of normal achieving (NLD) and learning disabled (LD) elementary students. The study replicated and extended the Cunningham and Stanovich (1990) study. Forty-eight primary-grade students (24 non-learning disabled; 24 learning disabled) received spelling training under three experimental conditions that involved different types of instructional activity: writing, sorting letter tiles, or typing on the computer. Results indicated no significant differences between the LD and NLD groups on words spelled correctly for any of the three conditions. Since the number of spelling words learned was low across conditions and groups, the number of correctly learned bigrams was examined. No within-group differences emerged for condition (writing, tile, computer); however, significant differences between the groups were found on number of bigrams learned for writing, tile, and computer, with the NLD group outperforming the LD group on all three conditions. Statistically significant time effects for bigrams were also noted for all three conditions. Unlike the Cunningham and Stanovich (1990) study, results did not replicate the superiority of the handwriting condition for the NLD group, nor was the handwriting condition found to be significantly more effective for the LD group.
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Lopez, Clémence, and Laurence Vaivre-Douret. "Concurrent and Predictive Validity of a Cycloid Loops Copy Task to Assess Handwriting Disorders in Children." Children 10, no. 2 (2023): 305. http://dx.doi.org/10.3390/children10020305.
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Handwriting disorders (HDs) are mainly assessed using script or cursive handwriting tasks. The most common is the scale for children’s handwriting, with a French adaptation (BHK). The present study aims to assess the concurrent validity of a pre-scriptural task (copying a line of cycloid loops) with the BHK for the diagnosis of HDs. Thirty-five primary school children (7 females, 28 males) with HD aged 6–11 years were recruited and compared to 331 typically developing children (TDC). Spatial/temporal/kinematic measures were collected using a digital pen on a paper. Posture and inter-segmental writing arm coordination were video recorded. A logistic regression statistical method, including a receiver-operating characteristic curve, was used to assess the ability of the task to predict HD. Gestural patterns were significantly less mature in HDs than in TDC (p < 0.05), and associated with poorer quality, less fluid, and slower drawing (p < 0.001). Moreover, good correlations between temporal and kinematic measures and the BHK scale were found. Number of strokes, total drawing time, in-air pauses times, and number of velocity peaks showed very good sensitivity (88%) and specificity (74%) to diagnose HDs. Consequently, the cycloid loops task is an easy, robust, and predictive tool for clinicians to identify HDs before the alphabet is mastered.
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Thomassen, Arnold J. W. M., and GP VanGalen. "Temporal Features of Handwriting: Challenges for Forensic Analysis." Journal of Forensic Document Examination 28 (December 31, 2018): 43–56. http://dx.doi.org/10.31974/jfde28-43-56.
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This paper looks at handwriting as a multi-stage motor process which develops over time and leaves its time-bound marks in the writing trajectory. It explains some of the time-based research methodology and points out a number of ‘dynamic’ features with potential relevance for forensic application. The final sections of the paper contain suggestions for research leading to the re-establishment of temporal features in the static trace, which could support the handwriting expert’s effort to decide on a document’s authorship and on the circumstances at the time of writing. Some of these suggestions present challenges for prolonged interdisciplinary research co-operation.
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Eremenko, Yu I., and Yu S. Olyunina. "Using the K-means Method to Identify a User by Keyboard Handwriting." Vestnik NSU. Series: Information Technologies 17, no. 2 (2019): 39–48. http://dx.doi.org/10.25205/1818-7900-2019-17-2-30-38.
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This article discusses the problem of protection against unauthorized access to data by identifying users by biometric characteristics – keyboard handwriting. To do this, the authors conducted a series of experiments to obtain a set of samples of keyboard handwriting, which are used as a biometric characteristic to identify the owner. To analyze the data and determine the author of a particular handwriting sample, the authors consider the k-means method. This method is one of the simplest and most effective statistical classification methods when the number of clusters is known in advance. To evaluate the effectiveness of this method for solving this problem, the authors propose using the coefficients of false access and false access denial, which are the main characteristics of biometric authentication systems. The results suggest that there are some limitations when using this method. They are related to the fact that this problem is poorly formalized and depends on many factors that can not be mathematically described. For example, the instability of the keyboard handwriting, which is due to changes in the psycho-physiological state of the user, the ergonomics of the keyboard and others. Given these features,the authors propose to solve the problem using methods based on intelligent data processing, which allow to detect hidden patterns and dependencies in the data flow.
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Stievano, Paolo, Davide M. Cammisuli, Silvia Michetti, Chiara Ceccolin, and Giovanni Anobile. "Cognitive processes underlying arithmetical skills in primary school: the role of fluency, handwriting, number line and number acuity." Neuropsychological Trends, no. 23 (April 2018): 115–38. http://dx.doi.org/10.7358/neur-2018-023-camm.
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Shyamala K.C, Saddam H. M. Issa,. "Improvement of cognitive ability of English language learners through writing skills." Psychology and Education Journal 58, no. 1 (2021): 5457–72. http://dx.doi.org/10.17762/pae.v58i1.1859.
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This paper explores the interrelation between the cognitive linguistic skills that are necessary for children to write on both words and texts. Three types of cognitive-linguistic abilities, which were considered to be more specifically interested in the text writing research, were transcriptional skills, ideation and syntax. The first two emerged out of a simplistic writing view (Berninger, 2010) while the predicted value of syntactic skills was based on an "extended triangle model" (Bishop & Snowling, 2011). The transcription skills in the present research have been operationalized to generate strokes in line with the proper order of stroke. With respect to the ideation, we tested the capacity of children to create orally sentences on such subjects and the requirements for grading are close to the criteria of text composition. Although good handwriting enhances student reading, improves skills, the process of abandoning handwriting has already started in a number of countries. This study paper reveals that handwriting is not only meant for
 primary school students, but also in high school education with regard to some important studies.
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Brown, C. Marlin “Lin.” "Comparison of Typing and Handwriting in “Two-Finger Typists”." Proceedings of the Human Factors Society Annual Meeting 32, no. 5 (1988): 381–85. http://dx.doi.org/10.1177/154193128803200533.
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Twelve subjects who are not touch typists, but have a median of ten years experience using computer keyboards performed two writing tasks: writing a short memorized passage and copying a four paragraph passage. Subjects performed each task once using a pen and paper and once using a display oriented text-editor. Typing speed was over five words per minute (wpm) faster than handwriting for both memorized and copied passages. Typing and writing were each about ten words per minute faster from memory than from copy. The number of errors was greater when typing from copy than in any other condition. These results suggest that for experienced two-finger typists, typing from a display-oriented document processor can be faster than handwriting.
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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 (2004): 1285–302. http://dx.doi.org/10.1142/s0218001404003666.
Texte intégralRésumé :
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.