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Parkinson’s disease (PD), the second most prevalent neurodegenerative disorder globally, afflicting approximately 10 million individuals, necessitates early detection for optimal management. In this paper, we propose deep learning models to discern Parkinson’s disease through the nuanced analysis of handwriting with the overall objective of achieving transparency and trustworthiness through the integration of Explainable and Interpretable AI.Leveraging transfer learning from well-established VGG16 and VGG19 architectures and introducing two bespoke CNN models (PD-Detect1 and PD-Detect2), we meticulously scrutinize diverse datasets (HandPD, NewhandPD, Parkinson Drawing) to ascertain the efficacy of our approach. LIME and SHAP Explainable AI techniques are employed to pinpoint specific regions of the spiral drawings that significantly influence the predictions made by the VGG16 and PD-Detect2 models. Additionally, Convolutional Filter Visualization and Grad-CAM are utilized to illustrate how the convolutional layers of the PD-Detect2 model function. Finally, LIME is applied to the PD-Detect2 model to identify visual markers of handwriting symptoms in the spiral drawing.Remarkable results underscore our reliance on VGG16 and VGG19 for precise identification, achieving an outstanding 100% accuracy in the waves drawing dataset. PD-Detect1 exhibits commendable performance with an accuracy of 94.44% in the meander of NewhandPD dataset, while VGG16 achieves an accuracy of 95%. VGG16 records 95% accuracy while PD-Detect2 achieves 85% accuracy in the spiral drawing dataset. Both results further bolstered to 100% with the application of classic data augmentation techniques. The Positive/Negative superpixels from LIME and SHAP highlight the key regions used by VGG16 and PD-Detect2 for predictions, while PD-Detect2 places more emphasis on disease-related features. Visualizing convolutional filters provided insight into the functionality of each layer within the PD-Detect2 model, while the Class Activation Maps produced by Grad-CAM highlighted the image regions most influential to the model’s decision. Ultimately, LIME’s superpixels identified visual markers of handwriting symptoms associated with Parkinson’s disease.Explainable AI and Interpretable AI enhance the quality of CNN models and support the decision-making process, enabling healthcare professionals to more accurately assess disease probability and monitor treatment responses. This leads to a more effective system for the early diagnosis of Parkinson’s disease through prediction and visual monitoring of handwriting symptoms. [ABSTRACT FROM AUTHOR]

In forensic handwriting analysis, it is crucial to understand the relative frequencies of findings relevant to the specific author, especially when using statistical methods. These are factored into the likelihoods used to determine the probabilities for the different authorship hypotheses. However, if ad hoc writings are included in the comparison materials, the representation of a comparison writer's habits can be distorted. An overrepresentation of certain creation time points can be avoided by treating ad hoc series of comparison writing samples as internally homogeneous agglomerates, incorporating only a single value per series into the average relative frequency of a given finding for a comparison writer. Additionally, the proposed approach produces finding counts largely independent of the length of the handwriting sample, which has a positive impact on the efficiency and cost‐effectiveness of the expert evaluation. [ABSTRACT FROM AUTHOR]

Robert Saudek, a Czech graphologist, journalist, diplomat, playwright, and novelist, was heavily influenced in his youth by Fritz Mauthner's critique of language. Saudek later became a pioneer in the field of psychological graphology. In this article, I examine the impact of Mauthner's critique on Saudek's work and evaluate whether Saudek's approach to graphology aligns with Mauthner's ideas. I argue that, although Saudek's graphology is rooted in Mauthner's critique of experimental psychology, there remains room for further development in the field of psychological graphology, centering on the analysis of language. With this in mind, I compare Saudek's method and contemporary conceptual metaphor theory. I further suggest that Saudek's extensive use of the autographs of well-known figures follows a method of working from examples, or specimens, which is not uncommon in the philosophy of science. Based on these findings, I propose a way of understanding Saudek's graphology that challenges its characterization as a pseudoscience. [ABSTRACT FROM AUTHOR]

Using biometric data for user authentication is a frequently addressed subject within the context of computer security. Despite significant advancements in technology, handwriting analysis continues to be the most common method of identifying individuals. There are two distinct types of handwriting recognition: offline and online. The first type involves the identification and interpretation of handwritten content obtained from an image, such as digitized human handwriting. The latter pertains to the identification of handwriting derived from digital writing performed on a touchpad or touchscreen. This research paper provides a comprehensive overview of the proposed apparatus specifically developed for collecting handwritten data. The acquisition of biometric information is conducted using a touchscreen device equipped with a variety of integrated and external sensors. In addition to acquiring signatures, the sensor-fusion-based configuration accumulates handwritten phrases, words, and individual letters to facilitate online user authentication. The proposed system can collect an extensive array of data. Specifically, it is possible to capture data related to stylus pressure, magnetometer readings, images, videos, and audio signals associated with handwriting executed on a tablet device. The study incorporates instances of gathered records, providing a graphical representation of the variation in handwriting among distinct users. The data obtained were additionally analyzed with regard to inter-person variability, intra-person variability, and classification potential. Initial findings from a limited sample of users demonstrate favorable results, intending to gather data from a more extensive user base. [ABSTRACT FROM AUTHOR]

Background: The neurodegenerative diseases like Alzheimer's disease (AD) can result in progressive decline in both cognitive functions and motor skills, which have critical need for accurate early diagnosis. However, current diagnosis approaches primarily rely on timely clinical magnetic resonance imaging (MRI) scans, which impede widely application for potential patients. Leveraging handwriting as a diagnostic tool offers significant potential for identifying AD in its early stages. Objective: This study aims to develop an efficient, rapid, and accurate method for early diagnosis of AD by utilizing handwriting analysis, a promising avenue due to its association with compromised motor skills in neurodegenerative diseases. Methods: We propose a novel methodology that leverages self-attention mechanisms for the early diagnosis of AD. Our approach integrates data from 25 distinct handwriting tasks available in the DARWIN (Diagnosis AlzheimeR WIth haNdwriting) dataset. Results: The Self-Attention model achieved an accuracy of 94.3% and an F1-score of 94.5%, outperforming other state-of-the-art models, including traditional machine learning and deep learning approaches. Specially, the Self-Attention model surpassed the previous best model, the convolutional neural networks, by approximately 4% in both accuracy and F1-score. Additionally, the model demonstrated superior precision (94.7%), sensitivity (94.5%), and specificity (94.1%), indicating high reliability and excellent identification of true positive and true negative cases, which is crucial in medical diagnostics. Conclusions: Handwriting analysis, powered by self-attention mechanisms, offers significant potential as a diagnostic tool for identifying AD in its early stages, providing an effective alternative to traditional MRI-based diagnosis. [ABSTRACT FROM AUTHOR]

The purpose of this study is to conduct a stylistic analysis of the selected poems of Emily Dickinson, a 19th-century poet, and Lang Leav, a contemporary poet, focused on the graphological level of the poems. The researcher selected four poems by Dickinson and Leav as the literary texts to be analyzed. Using comparative stylistic analysis, the study aims to look for the graphological features woven in the poems of Dickinson and Leav. The graphological features in the selected poems were also compared to find any significant similarities and differences between the styles of the two poets considering that they belong to different periods. Lastly, the graphological features were analyzed to know their possible effect on the meaning of the poems. As a result, archaism, eye dialect, unconventional use of em dash and capitalization, inconsistent number of lines in each stanza, and inconsistent number of words in each line were found in the selected poems of Dickinson while Leav's selected poems contain eye dialect, diacritics, minimal use of punctuation, absence of capitalization, italics, and the inconsistent number of words in each line of the poems. It was also revealed that their graphological features have more differences than similarities and the effect of these graphological features in the selected poems is mainly for emphasis and to add to the visual elements of the literary texts.

Graphology is a branch of science used to try to determine a person’s individual characteristics and details about that person based on a person’s handwriting. It was first started by the French clergyman-archaeologist Jean-Hippolyte Michon in 1871 and its use area has expanded in Europe over time. The science of graphology is a useful method that can also be used to evaluate people’s emotional development, give people career advice for the future, and even provide psychological analysis. For example, it is known that many international company managers today use the science of graphology to evaluate the personal characteristics of job applicants during or after the interview stage. However, there are also criticisms that the science of graphology cannot always produce clear results. To put it more clearly, in the science of evidence (criminology), a broad consensus on the validity of graphology studies has not been reached. Based on this point, the benefits and shortcomings of graphology were analyzed in the study and evaluations were made about the place and importance of graphology in archival science. In addition, the impact of graphology science on archiving science has also been evaluated by focusing on the connection between criminology science. The study, which focuses on the contribution of graphology science to archiving and information and document management sciences in particular, was completed by making various suggestions to support future academic studies.

Copyright of Iranian Journal of Applied Language Studies is the property of University of Sistan & Baluchestan and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the symptoms of Parkinson's disease (PD) is abnormal handwriting caused by motor dysfunction. The development of tablet technology opens up opportunities for an effective analysis of the writing process of people suffering from Parkinson's disease, aimed at supporting medical diagnosis using machine learning methods. Several approaches have been used and presented in the literature that discuss the analysis and understanding of images created during the writing of single words or sentences. In this study, we propose an analysis based on a sequence of sentences, which allows us to assess the evolution of writing over time. The study material consisted of handwriting image samples acquired in a group of 24 patients with PD and 24 healthy controls. The parameterization of the handwriting image samples was carried out using domain knowledge. Using the exhaustive search method, we selected the relevant features for the SVM algorithm performing binary classification. The results obtained were assessed using quality measures, including overall accuracy, which was 91.67%. The results were compared with competitive works on the same subject and seem to be better (a higher level of accuracy with a much smaller number of features than those presented by others). [ABSTRACT FROM AUTHOR]

Background: Parkinson's disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1–4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD's early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management. [ABSTRACT FROM AUTHOR]

Writing is a commonplace activity that individuals partake in regularly. However, the implications behind it are often overlooked. When we write, various psychological factors come into play as the pen creates letters on the paper. Handwriting analysis has long been a subject of study, attracting researchers from diverse disciplines such as graphology, psychology, paleography, neuroscience, criminology, and computer science. Among the promising applications of handwriting analysis is gender classification, where a system can predict the gender of a writer based on their handwriting style. Since each individual's handwriting is unique, and variations exist between the handwriting of different genders, an automatic gender classification system can exploit these differences to make predictions. This paper presents a deep-learning-based gender classification system specifically designed for Urdu handwriting. The proposed approach utilizes a CNN network trained and tested on a self-created dataset contributed by 200 distinct male and 200 female Urdu writers. Through this method, the gender classification system achieved an impressive overall accuracy of 99.63%. The results obtained demonstrate that our technique for Urdu handwriting-based writer identification surpasses existing approaches. In the future, we intend to explore transfer learning techniques to further advance this field. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disease that is not easily detected in the early stage. Handwriting and walking have been shown to be potential indicators of cognitive decline and are often affected by AD. Objective: This study proposes an assisted screening framework for AD based on multimodal analysis of handwriting and gait and explores whether using a combination of multiple modalities can improve the accuracy of single modality classification. Methods: We recruited 90 participants (38 AD patients and 52 healthy controls). The handwriting data was collected under four handwriting tasks using dot-matrix digital pens, and the gait data was collected using an electronic trail. The two kinds of features were fused as inputs for several different machine learning models (Logistic Regression, SVM, XGBoost, Adaboost, LightGBM), and the model performance was compared. Results: The accuracy of each model ranged from 71.95% to 96.17%. Among them, the model constructed by LightGBM had the best performance, with an accuracy of 96.17%, sensitivity of 95.32%, specificity of 96.78%, PPV of 95.94%, NPV of 96.74%, and AUC of 0.991. However, the highest accuracy of a single modality was 93.53%, which was achieved by XGBoost in gait features. Conclusions: The research results show that the combination of handwriting features and gait features can achieve better classification results than a single modality. In addition, the assisted screening model proposed in this study can achieve effective classification of AD, which has development and application prospects. [ABSTRACT FROM AUTHOR]

Handwritten character recognition remains a challenging task in various domains, including handwriting analysis and document digitization. This task poses a significant challenge because of the variability and complexity of handwriting. To address this, it is crucial to develop different methodologies that enhance accuracy and efficiency in recognition. This research evaluates the discrimination capacity of a classifier that combines together the Hu Moments features extraction technique and the k-nearest neighbors (KNN) algorithm for the purpose of handwritten character recognition. The authors worked with handwritten characters from the English alphabet where each character is represented in two handwritten forms. The average accuracy of recognition was 82.69%, that conforms the efficiency of the proposed classifier. This research contributes to the field of automated handwritten recognition and highlights the necessity for improved techniques in character analysis, which has implications for handwriting recognition in different contexts, linguistic studies, document digitization, and other applications where character recognition is crucial. This is the first study that aims to apply the combination of Hu Moments features extraction technique in combination with the KNN algorithm for purpose of handwritten character recognition. [ABSTRACT FROM AUTHOR]

For centuries, educators and experts have been engaged in a challenging endeavor to diagnose learning disabilities (LDs) such as dyslexia, dysgraphia, and dyscalculia. Traditionally, this problem has been addressed through longitudinal studies and specialized research. However, the complexity and time-consuming nature of this task have led to delays in the assessment process and have affected the learning journeys of countless children. Furthermore, earlier diagnosis will enable teachers and experts to provide individualized interventions and tailored support, enabling children with learning disabilities (LDs) to overcome their learning challenges and unlock their full potential. In our study, we offer a revolutionary solution: early detection of LDs using handwriting script analysis and employing advanced machine learning techniques. This research has been undertaken with the powerful diversity of the "Handyg23" dataset, which comprises handwriting samples collected from neurodegenerative and healthy controls. Furthermore, we have investigated the Beta-elliptic segmentation theory to extract spatial, temporal, and kinematic features, which serve as input to the classifier. The experiments have shown highly promising results, achieving an accuracy of 99% with gradient boosting. [ABSTRACT FROM AUTHOR]

Author verification is challenging because of the diversity in writing styles. We propose an enhanced handwriting verification method that combines handcrafted and automatically extracted features. The method uses a genetic algorithm to reduce the dimensionality of the feature set. We consider offline Bangla handwriting content and evaluate the proposed method using handcrafted features with a simple logistic regression, radial basis function network, and sequential minimal optimization as well as automatically extracted features using a convolutional neural network. The handcrafted features outperform the automatically extracted ones, achieving an average verification accuracy of 94.54% for 100 writers. The handcrafted features include Radon transform, histogram of oriented gradients, local phase quantization, and local binary patterns from interwriter and intrawriter content. The genetic algorithm reduces the feature dimensionality and selects salient features using a support vector machine. The top five experimental results are obtained from the optimal feature set selected using a consensus strategy. Comparisons with other methods and features confirm the satisfactory results. [ABSTRACT FROM AUTHOR]

Automated identification of mathematical expressions (MEs) is essential in transforming scientific and engineering documents into electronic form. Even though character and symbol recognizers have achieved commendable performance for digitizing documents, structure analysers still face a challenge in correctly interpreting the maths expressions. This review paper compares the salient aspects of past works dealing with structure analysis of printed and handwritten MEs. To the best of our knowledge, no previous work has done a systematic study of structural analysis methods in mathematical expression recognition. We present distinguishing aspects of different grammars and their production rules for semantic parsing of ME. Our study contributes by providing information on the existing datasets, their desirable properties, different evaluation measures, distinguishing aspects of techniques used and future research directions in structural analysis. [ABSTRACT FROM AUTHOR]

— The integration of handwriting recognition (HWR) with existing human-computer interaction paradigms presents an intriguing avenue for advancing user input methodologies. While gesture recognition systems have seen substantial advancements, they predominantly focus on discerning predefined gestures rather than the intricate symbols and shapes inherent to handwriting. This research embarks on a comprehensive exploration of Air Handwriting Recognition (AHR) as a novel input modality, capturing and interpreting pen movements in a free space environment, akin to writing with an imaginary pen. The distinction between traditional HWR and AHR is pivotal for understanding the unique capabilities and challenges of the latter. Air handwriting, a manifestation of hand movements devoid of tangible mediums such as paper or digital pads, emerges as an alternative input method in scenarios where conventional typing is cumbersome or impractical. This modality not only accommodates diverse user environments, including mobile and pen-based computing, but also offers a natural and intuitive means of data entry and interaction. The study delves into a range of detection techniques tailored for AHR, each with its distinct advantages and challenges. Optical sensors offer high-resolution tracking but may be susceptible to ambient lighting conditions. Infrared sensors, operating based on heat signatures, excel in low-light environments but may require frequent calibration. Ultrasonic sensors leverage sound waves for precise tracking but can be affected by acoustic interferences. Capacitive sensors, sensitive to touch and proximity, offer versatility but may require direct contact or close proximity to the writing surface. Despite the promising potential of AHR, several challenges necessitate innovative solutions. Noise interference, stemming from external factors or inherent sensor limitations, can compromise detection accuracy. Variability in handwriting styles across individuals introduces complexities in standardizing recognition algorithms. The demand for real-time recognition further amplifies computational requirements, necessitating efficient algorithms and processing techniques. Moreover, ensuring high accuracy and implementing robust error correction mechanisms are paramount to enhancing user trust and system reliability. Beyond these challenges, the applications of AHR are manifold and transformative. It holds the potential to revolutionize gesture-based user interfaces, bridging the gap between natural human movements and digital interactions. In virtual and augmented reality environments, AHR can enrich user experiences by enabling intuitive interaction with virtual objects and interfaces. Additionally, AHR can facilitate real-time translation of sign language, empowering individuals with hearing impairments to communicate more effectively. Furthermore, the technology could be instrumental in forensic handwriting analysis, aiding investigators in nalyzing and authenticating handwritten documents. In conclusion, while AHR is an emerging technology with inherent complexities and challenges, its potential to reshape human-computer interaction paradigms is undeniable. This paper serves as a comprehensive exploration into the realm of AHR, offering insights into its definition, detection techniques, challenges, and transformative applications. As this field is evolving, interdisciplinary research and collaboration will be critical in unlocking the full potential of AHR and paving the way for a future where handwriting and gesture-based inputs harmoniously coalesce in diverse computing environments. [ABSTRACT FROM AUTHOR]

In this article is presented a case study from the graphic forensic expertise practice, in which the main problem consists in diagnosing the conditions of execution of manuscript texts and signatures in four cash loan receipts. The case seemed worthy of attention and useful to be known by those interested in such objects of expertise, being quite difficult the demonstration part, exclusively with graphoscopic instruments. Although graphology, in this case graphopathology, offers a variety of solutions in this regard, in this situation it could not be used, having no legal coverage in the Romanian space. As a matter of principle, there should be no deficiencies in the forensic research of graphic objects executed in conditions of old age and illness, taking into account the existence of methodical instructions, developed in the early 80s of the last century, which to this day still prove to be current and lucrative, as they offer the successful possibility to discover the fact of execution of writing and signatures by this category of persons (old people, sick). At the same time, these instructions require improvement, experimental research, monitoring of modern trends of physiological and psychological development of man. We hope that our work will put a favorable beginning in the accumulation of expert experience in the segment of forensic investigation of writing and signatures executed in conditions of illness, old age, with a remedial contribution to this chapter of graphic forensic expertise methodics. [ABSTRACT FROM AUTHOR]

A person's personality is a combination of their unique traits and attributes, influenced by their development, ideals, and connections with the community, life experiences, routines, and abilities. Handwriting characteristics can reveal aspects of one's personality, behaviour, and even psychiatric traits. Graphology, the study of handwriting, is used to examine these traits. Previous studies have focused on a limited number of handwriting characteristics, but graphology suggests that a wide range of stroke qualities can reflect psychological traits. This survey explores various methods for feature extraction to predict personality based on handwriting, bridging the gap between personality psychology and graphology. Understanding handwriting features with psychological backing can aid in predicting personality traits, promoting computer-based graphology as a tool for personality prediction. [ABSTRACT FROM AUTHOR]

Introduction: Handwriting deteriorates proportionally to the writer's cognitive state. Such knowledge is of special importance in the case of a contested will, where dementia of the testator is claimed, but medical records are often insufficient to decide what the testator's cognitive state really was. By contrast, if the will is handwritten, handwriting analysis allows us to gauge the testator's cognitive state at the precise moment when he/she was writing the will. However, quantitative methods are needed to precisely evaluate whether the writer's cognitive state was normal or not. We aim to provide a test that quantifies handwriting deterioration to gauge a writer's cognitive state. Methods: We consecutively enrolled patients who came for the evaluation of cognitive impairment at the Outpatient Clinic for Cognitive Impairment of the Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Sciences (DINOGMI) of the University of Genoa, Italy. Additionally, we enrolled their caregivers. We asked them to write a short text by hand, and we administered the Mini Mental State Examination (MMSE). Then, we investigated which handwriting parameters correlated with cognitive state as gauged by the MMSE. Results: Our study found that a single score, which we called the COGnitive Impairment Through hAndwriTing (COGITAT) score, reliably allows us to predict the writer's cognitive state. Conclusion: The COGITAT score may be a valuable tool to gage the cognitive state of the author of a manuscript. This score may be especially useful in contested handwritten wills, where clinical examination of the writer is precluded. [ABSTRACT FROM AUTHOR]

This study aims to investigate using domestication and foreignization to render stylistic devices in two Arabic translations of Hemingway’s Men without Women, namely Farea’s translation and Nassar’s translation. The study follows the descriptive, analytical comparative method. The researcher applies a detailed descriptive content-based analysis of the ST stylistic devices at three levels: syntax, lexicology and graphology. The criteria of the study analysis are on two validated checklists of the ST stylistic devices and their translations alongside the procedures adopted by the translators. The study focuses on the applied procedures that identify the translators’ tendencies in using domestication or foreignization. Transliteration and literal translation are regarded as foreignization procedures whereas cultural substitution, adaptation, equivalence, paraphrase, modulation, transposition, omission and addition constitute domestication ones. To determine to what extent these two methods are utilized, frequencies and percentages are quantitatively provided. In light of the study's theoretical framework, data are gathered, categorized, and scrutinized. The results show that both domestication and foreignization are manifested in the two translations with different proportions. Farea’s translation favours domestication due to the great use of domesticating procedures particularly adaptation. Yet, Nassar’s translation prefers foreignization due to the higher usage of literal translation and transliteration. [ABSTRACT FROM AUTHOR]

The article focuses on John Kinsella's exploration of combining photography and poetry into a unified artistic expression. Topics include his early experiments with photo essays as poems, the development of his 'concretions' approach intertwining text and landscape, and his ongoing 'Graphology' series, which examines the act of writing and environmental activism through low-tech, light-sensitive engagements.

Copyright of Lernen und Lernstörungen is the property of Hogrefe AG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The features of cerebral small vessel disease (CSVD) range from occurrence of asymptomatic radiological markers to symptomatic characteristics that include cognitive deficits and gait decline. The aim of the present study was to examine whether handwriting movement is abnormal in older people with CSVD through handwriting and drawing tasks using digitized handwriting kinematic assessment technology. Methods: Older subjects (n = 60) were grouped according to Fazekas score, with 16 in the Severe CSVD group, 12 in the Non-severe group and 32 in the Healthy group. Kinematic data were recorded and analyzed during handwriting and drawing tasks: signature; writing of Chinese characters ("正" and "永"); and Archimedes' spiral drawing. Results: The Severe CSVD group showed lower velocity and higher tortuosity during signature writing, lower velocity of stroke #4 of "正" and vertical size of "永" than did the Non-severe and Healthy groups. Both Severe CSVD and Non-severe CSVD subjects displayed higher average normalized jerk than did the Healthy group. Partial correlation analysis adjusting for age, gender, education, and mini-mental state evaluation (MMSE) showed that CSVD burden was positively associated with tortuosity of signature and average normalized jerk of Archimedes' spiral, and was negatively associated with velocity of strokes #3 and #4 of "正", as well as vertical size of "永". Conclusions: Older adults with CSVD showed abnormal handwriting movement. And the handwriting abnormalities captured by digitized handwriting analysis were correlated with CSVD severity in users of simplified Chinese characters. [ABSTRACT FROM AUTHOR]

Teaching materials may be considered to be effective if their preparation is adjusted to the level of students’ internal potential and cognitive abilities. Therefore, graphology is required to assist in identifying students’ inner potential for the results of the graphological analysis to be used as a basis for preparing materials. This research aims to discover students’ inner potentials using graphological sciences, which can later be used to consider the criteria for preparing teaching materials. This research used a descriptive qualitative research method since this research produces data in the form of descriptive data (writing). The research’s subject is the handwriting of students of X-1 class Bekasi Regency. The stages of this research are handwriting analysis, triangulation through interviews, grouping results. The data used is in the form of student handwriting with an assessment of the characteristics of student writing as a whole in terms of the slope of writing, the direction of the writing line, the size of the writing, the spacing between words, and the spacing between lines. Qualitative research data analysis techniques are carried out with the stages of analysis implementation according to Miles and Huberman with stages: (1) Data reduction, (2) Presentation of data; (3) Drawing conclusions. According to the study’s result, it was discovered that the students’ inner potentials were very diverse. As a result, the form in which teaching materials are prepared must likewise be varied.

Teachers must attempt to learn about students' learning styles, psychological conditions, emotions, and personal conditions through non-cognitive diagnostic assessments. No one can learn purely visual, auditory, or kinesthetic since they are all prejudiced. Therefore, another non-cognitive approach is needed, specifically graphology. The study aims to determine changes in learning model methodology application that are appropriate to the students' conditions. The research method used by the researcher was a qualitative descriptive method. This study describes the results of analyzing students' handwriting in X-1 class of SMA Mutiara Islami Plus Bekasi. Several categories were analyzed, including the writing slant, writing line slant, writing size, word, and line spacing in writing, as well as the letter analysis (a, t, and j). The analysis results were organized in differentiated learning using a method of Inquiry Training Learning. This handwriting analysis can assist educators in discovering and determining students' interests and talents further.

A person's handwriting is different and unique, even though it looks similar it is certainly not the same as someone else's writing. One's personality traits can be identified based on handwriting. One of the implementations is (handwriting recognition). To identify a person's personality, it can be classified by handwriting using the 'Graphology' field. The computational system to identify handwritten images can use the Convulution Neural Network method. Using the CNN method is expected to produce good accuracy with a low error rate. The CNN method is able to predict a person's personality through manuscripts as images. In addition, to increase the diversity of classifications, the Least Squared Classifiers method is needed. . LS Classifiers are designed to increase the variety of CNN methods in feature extraction and classification. The LS Classifier method is a classification method that estimates the w parameter vector and takes the best linear classifier based on the w parameter vector. Research has functions for users, including to find out someone's personality, especially extrovert and introvert personality. In this study CNN serves as Feature Extraction to classify Image and Ls Classifiers serves to increase diversity into 2 personality groups. The level of accuracy of the performance of the CNN & Ls Classifiers method in carrying out feature extraction and classification of handwritten images in determining personality has a good level of accuracy.

There are many ways that can be done to find out the individual characteristics of each student. The purpose of this study was to determine the internal potential of 10th Grade “Halimah” Students by using graphological analysis based on general characteristics, which is the slope of the writing direction, the slope of the writing lines, the spacing between words, and the spacing between lines, and based on the general characteristics, which are the letters "I", "R", and "G". This study uses a qualitative descriptive research method. This method is used to describe the general description of the research, which are the characteristics of handwriting with the internal potential of students and to interpret graphology in several aspects. Then, this type of descriptive research is also capable of solving practical problems in the development of science, especially for graphological research. The stages of this research are handwriting analysis, triangulation through interviews, grouping results. The results of the analysis show that the internal potential of students is variative, so that appropriate learning methods or approaches are needed in order to achieve individual student learning goals

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problems related to unique personality identification are faced by various fields like psychology, criminal justice, and forensics investigations when hiring employees and assigning responsibilities. Presently, this identification is made manually by appointing a graphologist, which involves a cost. A person's unique personality is predicted using graphological analyses, which helps identify the person's truth or uniqueness. This quality allows the various organisations to understand the person's mental status, analyse the person's handwriting, assign the work to newly appointed employees or existing employees, and save the employer's time. The personality prediction is made using the person's handwriting; the parameters taken from handwriting are the slant of the words, the margin left by the writer, the baseline, and the size of the letter. The proposed work gives optimal values of input parameters for better accuracy in personality prediction. The proposed solution is implemented using different steps, which include preprocessing and segmentation, extraction of image features, trait acquisition, training and testing of the model, and personality prediction. The proposed work solves the problems using a support vector machine with a classifier with an accuracy of 95.05% and the personality traits of the writer, which is better than existing work. Apart from this, in the image preprocessing steps, the optimal inversion value was 255 pixels. For dilation, the kernel value of 5100 was taken. For line segmentation of an image, the optimal anchor value lies between 5000 and 7000. [ABSTRACT FROM AUTHOR]

There is a growing interest in the possibility of artificial neural networks' applications in forensics. Extensive research has been published on this subject, especially in the field of handwriting examination. However, it seldom discusses forensic and legal standards, which are the most fundamental of conditions for the acceptance of artificial neural networks in forensics. From the perspective of handwriting analysis, we have exemplified and systematized general methods for an informal falsification of artificial neural networks applied to verification of offline handwritten documents' authorship. These approaches should be generally effective against applications of neural networks in forensics, aimed to objectively expose and prove models as unreliable. [ABSTRACT FROM AUTHOR]

Psi Chi, the International Honor Society in Psychology, has a program called NICE that promotes collaborative psychological research across cultures. The first project, NICE: Crowd, focused on understanding family dynamics in different countries. This initiative increases the diversity and credibility of research by involving participants and researchers from multiple countries. Another study discussed in the text focuses on the development of quality metrics for handwriting analysis, aiming to improve the accuracy and reliability of psychological assessments. The text also covers various research studies and techniques in psychology, providing valuable insights and tools for researchers and practitioners in the field. [Extracted from the article]

This document provides a summary of various studies and assessments conducted in the field of psychology. Topics covered include the validation of a questionnaire on drinking motives, the development of a picture-based personality inventory, the integration of handwriting analysis into legal and forensic psychological assessment, and the development of a comprehensive assessment battery of language development. The studies explore different psychological constructs and their measurement, providing valuable insights for researchers and practitioners in the field. [Extracted from the article]

Machine learning‐based score likelihood ratios (SLRs) have emerged as alternatives to traditional likelihood ratios and Bayes factors to quantify the value of evidence when contrasting two opposing propositions. When developing a conventional statistical model is infeasible, machine learning can be used to construct a (dis)similarity score for complex data and estimate the ratio of the conditional distributions of the scores. Under the common source problem, the opposing propositions address if two items come from the same source. To develop their SLRs, practitioners create datasets using pairwise comparisons from a background population sample. These comparisons result in a complex dependence structure that violates the independence assumption made by many popular methods. We propose a resampling step to remedy this lack of independence and an ensemble approach to enhance the performance of SLR systems. First, we introduce a source‐aware resampling plan to construct datasets where the independence assumption is met. Using these newly created sets, we train multiple base SLRs and aggregate their outputs into a final value of evidence. Our experimental results show that this ensemble SLR can outperform a traditional SLR approach in terms of the rate of misleading evidence and discriminatory power and present more consistent results. [ABSTRACT FROM AUTHOR]

Handwriting recognition and analysis has been an active area of research in the last two decades. Handwriting analysis is being studied in various fields of science, such as graphology, neurology, psychology, and computer science. Furthermore, automated handwriting analysis has several applications, including forensic, security, medical, and disease prediction. This paper presents the most recent handwriting analysis techniques and advancements available in the literature for age and gender classification/detection. Different steps, including feature extraction and classification, frequently used in the literature for age and gender detection, are discussed, and the presented works are classified according to the applied feature extraction and classification methods. The online and offline benchmark databases are also reviewed. We used a text mining technique to perform a quantitative content analysis of the presented research and better understand the co-occurrence network diagrams of age and gender classification/detection. This study is a valuable resource that provides new research directions to students and researchers interested in this field for further research and investigation. [ABSTRACT FROM AUTHOR]

The present study examines Saudek’s novel Diplomats (1921) from three perspectives: first, with regard to the (biographical) circumstances of its creation and publication; second, in the gradual development of its concrete form and fields of meaning; third, with an emphasis on the ways in which it was realized. The middle part of the study is the most dominant, evoking (in excerpts and comments) specific elements and constraints of Saudek’s novel structure — centred on the feminist intermingling of the story of Pygmalion with Christ’s (or apostolic) gestation, in accordance with a tendency of the period to bring together the national and universal horizons of salvation, and to confront the temporal connections and confrontations with stimuli from the outside world. To understand their present and future, the main protagonists of the novel are constructed as initiates in the art of graphology, able to identify the handwriting of famous writers. They believe, moreover, that they can recognize the ‘handwriting of history’: that is, the contours of what is to come. At the same time, they do not want to remain mere contemplative readers of this historical nature, and they go in search of someone who can change the course of the world to meet their ideal, namely that of a peaceful world. They find this in the figure of T. G. Masaryk.

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Legibility, which can be defined as the format of the text, is one of the key elements in teaching writing. School starting age can affect students' learning and writing, and determining it can give an idea about what type of work to be undertaken to ensure the legibility of the handwriting in the early reading and writing processes. The possible effect of the school starting age on the legibility of handwriting was the basis of this study. As such, the study aimed to examine the straight basic writings of primary school first grade students in terms of legibility based on the school starting age. The study was designed as a survey in the quantitative research method. The study group consists of 513 primary school first grade students. The convenience sampling method was chosen to determine the study group. The students who participated in the study voluntarily consisted of primary school first-year students who passed into literacy in May of the 2022-2023 academic year. Data were obtained using the Multidimensional Legibility Scale. The Mann-Whitney U test was used in the analysis of the data. Results revealed that the handwriting legibility of the students was moderate regarding the dimensions of slant, spacing, size, and line awareness, but not legible in terms of 'form'. While the results of this study show that the average handwriting legibility of girls is higher than that of boys, there is no statistically significant difference between the age of school entry and handwriting legibility. [ABSTRACT FROM AUTHOR]

We develop a statistical approach to model handwriting that accommodates all styles of writing (cursive, print, connected print). The goal is to compute a posterior probability of writership of a questioned document given a closed set of candidate writers. Such probabilistic statements can support examiner conclusions and enable a quantitative forensic evaluation of handwritten documents. Writing is treated as a sequence of disjoint graphical structures, which are extracted using an automated and open‐source process. The graphs are grouped based on the similarity of their shapes through a K‐means clustering template. A person's writing pattern can be characterized by the rate at which graphs are emitted to each cluster. The cluster memberships serve as data for a Bayesian hierarchical model with a mixture component. The rate of mixing between two parameters in the hierarchy indicates writing style. [ABSTRACT FROM AUTHOR]

Handwriting contains a lot of information about the person writing it and is a sign of personality traits represented by neurological patterns in the brain. In other words, our brain and subconscious actually shape our character as a result of our habits. It is possible to get an idea about the mood of the individual by examining the handwriting. Joy, sadness, anger and anxiety are some of them. In this study, handwritings of people belonging to different professions and age groups were collected. Feature extraction methods was applied on these articles by applying word and line detection, slant, pressure, page layout and similar image processing methods. The obtained features formed the inputs of the dataset. Personality traits such as calm, optimistic, emotional, extrovert, which were estimated using graphology, were added to the dataset as outputs. Then, this dataset was applied to Random Forest (RF), Naive Bayes (NB), Decision Tree, Support Vector Machines (SVM), Logistic Regression, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) algorithms. According to the performance metrics used, the Random Forest algorithm gave the most successful results in terms of accuracy, precision and f1-score metrics. For this algorithm, the accuracy, precision, recall and f1 score values were found to be 0.90, 0.91, 0.84 and 0.85, respectively. Furthermore, the results of the personality analysis were compared with the results of the personality test performed by the expert psychologist. As a result of this comparison, it was seen that there was a 73% match. [ABSTRACT FROM AUTHOR]

• We consider large-scale supervised pre-training on a carefully designed synthetic dataset of word images. • We obtain robust writer's style representations, independent of the semantic content of the image. • We leverage the obtained representations for handwriting analysis tasks on real images from benchmark datasets. • Experiments demonstrate the suitability of our approach and its competitiveness compared to task-specific state-of-the-art. In this work, we explore massive pre-training on synthetic word images for enhancing the performance on four benchmark downstream handwriting analysis tasks. To this end, we build a large synthetic dataset of word images rendered in several handwriting fonts, which offers a complete supervision signal. We use it to train a simple convolutional neural network (ConvNet) with a fully supervised objective. The vector representations of the images obtained from the pre-trained ConvNet can then be considered as encodings of the handwriting style. We exploit such representations for Writer Retrieval, Writer Identification, Writer Verification, and Writer Classification and demonstrate that our pre-training strategy allows extracting rich representations of the writers' style that enable the aforementioned tasks with competitive results with respect to task-specific State-of-the-Art approaches. [ABSTRACT FROM AUTHOR]

The writer identification system identifies individuals based on their handwriting is a frequent topic in biometric authentication and verification systems. Due to its importance, numerous studies have been conducted in various languages. Researchers have established several learning methods for writer identification including supervised and unsupervised learning. However, supervised methods require a large amount of annotation data, which is impossible in most scenarios. On the other hand, unsupervised writer identification methods may be limited and dependent on feature extraction that cannot provide the proper objectives to the architecture and be misinterpreted. This paper introduces an unsupervised writer identification system that analyzes the data and recognizes the writer based on the inter-feature relations of the data to resolve the uncertainty of the features. A pairwise architecturebased Autoembedder was applied to generate clusterable embeddings for handwritten text images. Furthermore, the trained baseline architecture generates the embedding of the data image, and the K-means algorithm is used to distinguish the embedding of individual writers. The proposed model utilized the IAM dataset for the experiment as it is inconsistent with contributions from the authors but is easily accessible for writer identification tasks. In addition, traditional evaluation metrics are used in the proposed model. Finally, the proposed model is compared with a few unsupervised models, and it outperformed the state-of-the-art deep convolutional architectures in recognizing writers based on unlabeled data. [ABSTRACT FROM AUTHOR]

Graphology is the psycho-analysis study of personality and individual characteristics of writers based on the handwriting types. Graphology is a time-consuming and complicated task. Due to small number of expert graphologists, computerized measurement of graphology is an essential need. In this research, in addition to extraction of handwriting features, it is tried to categorize them in terms of regularity and inequality of letters and recognize person’s characteristics via graphology tables. This classification includes these types of handwriting: spontaneous, with difference in pen pressure, uncoordinated, coordinated with partial variance, uniform, sharp, regular, with inequality of letter’s inflexion and unequal. In this paper, 140 typical Farsi handwriting samples are used which are interpreted after giving MMPI (Minnesota Multiphasic Personality Inventory) questionnaire. The novel proposed features are used as the Mamdani fuzzy classifier inputs. Variance of baseline curves, variance of distances between words, variance of vertical letter height, variance of pen width, number of zero, acute and obtuse angles and their ratios to the number of text lines and slope angle are used as features for Mamdani fuzzy classifier inputs. In this study, there are nine handwriting types for personality recognition and 24 rules are defined for the nine fuzzy systems. Compared to our ground truth (MMPI results for the handwriting database), the proposed method showed an accuracy of 82.5% for personality recognition that demonstrated promising results.

Reading a person's character through handwriting can be learned through a science called "Graphology". The main limitation of this research problem is "how to analyze the character of Sukarno using a graphological approach and a historical approach?” The stages of handwriting analysis are an intuitive process, using guidelines, and integrating the first and second stages. The historical method is used to obtain a description of the character of a historical figure for comparison with the results of the graphological analysis. The results showed that the graphology and historical studies did not contradict and instead clarified each other. Historical facts further strengthen the results of the graphological analysis. However, there are some contradictions in historical and graphological findings that require in-depth analysis. In Sukarno’ study, contradictions include the social spirit and dominant character as well as efforts to maintain distance from others, leadership from the aspect of technical organizing, and emotional stability or instability.