Digital Handwriting Research Articles

Signature size and individual preferences

In this digital economy, customers' handwriting, such as signatures and related characteristics, can be easily collected and quantitatively analyzed. From the perspective of decision-making, obtaining information from signature characteristics is an important data-driven business leverage. In this regard, can we infer individual preferences from signatures? Through a large-scale laboratory experiment, this study measured the participants’ risk and time preferences and collected their signature information in order to determine the influence of behavioral preferences on their signatures. Based on the empirical results, the higher the risk and ambiguity aversion of the participants, the smaller were their signature sizes. Moreover, the greater their level of patience, the more likely their signatures showed a trend of increasing size. Overall, this study fills a research gap in signature-related behavior by highlighting the application of text mining in digital handwriting, especially in broad business settings such as the insurance industry.

Data Testing in Digital Transmission: Tamil Literature

The developing area of Optical Character Recognition (OCR) is digital handwriting recognition. Manual writing is replaced by a digital writing pad. The font and shape of the letters vary while writing digitally. The writer’s digital pen pressure and position on the digital pad cause covert text file problems during OCR recognition. When converting OCR to text, an error occurs because of the variations in letter shapes. In languages like Tamil, Chinese, Arabic, and Telugu, where the alphabet is made up of bends, curves, and rings, the aforementioned issue occurs. Tamil has more word mistakes in OCR-to-text conversion because the alphabet comprises curves and angles that must be correctly transcribed. The ResNet (Residual Neural Network) Two-Stage Bottleneck Architecture (RTSBA) is suggested in this paper. In order to recognize text written in Tamil on a digital writing pad, this article suggests using ResNet (Residual Neural Network) Two-Stage Bottleneck Architecture (RTSBA). The suggested RTSBA reduces the complexity of the Tamil alphabet recognition problem by using two distinct phases of neural networks. There are fewer inputs and variables in the early stages. Time and computational complexity are minimized in the last phase. A two-channel and two-stream transformer, long short-term memory, Inception-v3, recurrent neural networks, convolutional neural networks, and other conventional algorithms have been compared to the suggested algorithm. The digital writing pad-handwritten and HP lab datasets demonstrate the effectiveness of proposed methods like RTSBA, which yield accuracy rates of 98.7% and 97.1%, respectively.

Digital handwriting characteristics for dysgraphia detection using artificial neural network

Despite all of the technical advancements in writing and text editing with keyboards on numerous devices, writing with a pen remains a fundamental ability in modern human existence. Handwriting disabilities are referred to as dysgraphia. Nonetheless, how well they are taught to write in school, 10-30% of children never attain a respectable level of handwriting. Early identification is critical because it can help children avoid difficulties in their behavioral and academic development. On blank papers attached to digital tablets, 280 individuals were asked to complete the concise evaluation scale for children’s handwriting (BHK), with 218 having typical handwriting and 62 having dysgraphia. In addition to their age and BHK quality and speed scores, 12 variables identifying digital handwriting across several domains (static, kinematic, pressure, and tilt) were collected. In this paper, we provided a rapid and automated dysgraphia classification approach using an artificial neural network (ANN) model. Using digital handwriting traits as an input to the ANN approach, the prediction findings were encouraging and very accurate, reaching 96% accuracy, and they could lead to the development of a new self-administered dysgraphia screening tool.

A Framework for Determining the Big Five Personality Traits Using Machine Learning Classification through Graphology

Along with the progress of the times, the development of graphology has changed towards computerization. The fundamental problem in automated graphology is how to determine personality traits through digital handwriting using the principles of graphology. Although various models and approaches have been developed in research related to automated graphology, there are still obstacles to overcome such as the selection of preprocessing techniques and image processing algorithms to extract handwriting features and proper classification techniques to get maximum accuracy. Therefore, this study aims to design a reliable framework using image processing and machine learning approaches such as filtering, thresholding, and normalization to determine the personality traits through handwriting features. Then, handwriting features are classified according to the Big Five model. Experiments using the decision tree, SVM (kernel RBF), and KNN produced an accuracy above 99%. These results indicated that the proposed framework can be well applied to predict the personality of the Big Five model through handwriting analysis features.

Digital Handwriting Recognition Using Hand Tracking by Using Media Pipe and OPENCV Libraries

Abstract: While in taking online classes or in day-to-day activities writing is more important task, as we are living in digital age which means most of our tasks are been shifted to digital trends such as like paying bills, booking tickets, emotion tracking etc., among this writing is at most important task weather you share ideas plan for a particular task taking notes, in this data age stats show humans more often prefer to write and use about 100 pages per average. It is quite our instinct to understand and represent any information which is written in handwriting, but when it comes to digitalworld though they are gesture based, touch based methods they couldn’t match the natural hand-write representation. So, we come with solution for this problem forsome extent by using hand tracking and hand landmark position representation whichis offered by media pipe library form google. This technique uses image processing (OpenCV) to capture video from webcam which will be given to our python application which uses media pipe library to recognize hand and represents it as 0-20 landmark positions. Which will be used to find index finger for writing and use two fingers for removing or erasing pre-written information. This type of application isvery useful in a day to day basic activates for students, teachers and makers in order to easily plan and organize projects, notes etc., Though this technique neither match areal writing experience nor the writing tablet for computers and smartphones which can translate our writing into digital notebook, it does not require any additional equipment and can be easily used to if practiced. This type of application can improvise the understanding by allowing users to explain in a whiteboard like environment in online education.

A feature fusion sequence learning approach for quantitative analysis of tremor symptoms based on digital handwriting

Essential tremor and Parkinson's disease are common movement disorders, and early diagnosis and evaluation are critical to managing these diseases. Currently, laboratory tests are the only way to diagnose and assess tremor symptoms. Analysis of a patient's fine motor control, especially handwriting, is a powerful tool for disease assessment. However, traditional visual assessment methods by neurologists typically lead to biased diagnostic results due to some subjective factors. Therefore, it is necessary to automatically identify and quantify the captured motion events with the help of artificial intelligence in combination with the various dynamic attributes encapsulated in the digital ink features, such as pen pressure, stroke speed, handwriting variability, etc. In this paper, a novel Transformer deep-learning model is developed for sequence learning of electronic handwriting to effectively evaluate its potential in aiding the diagnosis of tremor symptoms. The one-dimensional convolution with an ingenious fusion attention mechanism is applied to the original pen sensor signal sequences and derived features are used as the embedding layer of the Transformer encoder part, and the global dynamic features are fused before the decision layer. Our proposed system performs excellent on private datasets and outperforms state-of-the-art methods on the PaHaW dataset.

A robust cross-ethnic digital handwriting recognition method based on deep learning

With the development of deep learning technology, handwritten character recognition has become the basic hotspot of research. At the same time, the recognition of handwriting in minority languages is gradually worthy of attention. In this paper, a robust and novel improved deep convolutional neural network method is proposed to recognize normal digital handwriting images and minority digital handwritten images (Yi language). To improve the recognition accuracy of handwritten fonts, there are four improvements in our work. Firstly, separable convolution is adopted in the network to cope with the problem of large parameters. Secondly, data augmentation techniques (Gaussian noise, color perturbation, and random rotation) are utilized to improve the expression ability of the dataset and the generalization capabilities of the model. Finally, the shortcut block and the weight initialization are introduced in our network for the problems of network degradation and gradient saturation. Based on deep learning technology, this paper studies the classification and recognition of handwritten digits, and improves the accuracy of the recognition in numbers such as invoices in financial bills. The recognition accuracy of 99.82% has been achieved on MNIST dataset, and 98.79% on the self-building dataset of Yi handwritten digits (YHDD).

Influence of neurodegenerative diseases on handwriting

Handwriting, like all other behavior, is regulated by the brain. This is typically an unconscious mechanism that is closely related to brain impulses. Any mental disorder can impair the forearm's kinetic motions, as can be seen in the subject's handwriting. It's crucial to focus on how a subject write rather than what a subject write if you want to understand health and mental issues. The aim of this analysis study is to learn about the changes that occur in an individual's handwriting characteristics during psychological problems, or when the subject is in a troubled state of mind. Calligraphy The study and analysis of handwriting, especially in relation to human psychology, is known as graphology. It has been discovered that features related to motion, time, and pressure are very useful and could be used to diagnose health and mental disorders using a manual or digital handwriting analysis approach.

A versatile computational algorithm for time-series data analysis and machine-learning models

Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.

Contactless Kinesthetic Feedback to Support Handwriting Using Magnetic Force.

Handwriting is a fundamental human skill that is essential for communication yet is one of the most complex skills to be mastered. Pen-based interaction with touchscreen devices are increasingly used in digital handwriting practices to simulate pen and paper experience, but are mostly based on auditory-visual feedback. Given that handwriting relies on visual and motor skills, haptic feedback is recently explored to augment audio-visual systems to further support the handwriting process. In this article, we present an assistive platform entitled KATIB (means writer in Arabic) that provides high fidelity kinesthetic feedback, in addition to audio-visual feedback, to support handwriting using magnetic forces. We propose novel contactless kinesthetic guidance methods, namely proactive and retroactive guidance, to guide the handwriting stylus along a desirable trajectory based on position control. Detaching the handwriting stylus from any mechanical device enables learners to have full control over grasping and moving at their own pace and style. The proposed platform is characterized for haptic interaction. Finally, a psychophysical experiment is conducted to validate that the kinesthetic guidance is perceivable and beneficial as a sensory feedback using a novel handwriting copy task. Contactless kinesthetic feedback seems to play a significant role in supporting digital handwriting by influencing the kinematics of the handwriting process.

On the Scope of Criminalistic Habitoscopy in the Implementation of Measures Against Modern Cyber Crime

The article presents an overview of modern possibilities of criminalistic habitoscopy in the conditions of global digitization. Methodological recommendations on the investigation and solution of cybercrimes should have a serious research and methodological basis, and criminalistic habitoscopy could provide such a basis. In the situation of global digitization and the use of virtual channels of communication (video conference calls), the characteristics of a persons outward features should include perceptibility, or the ability of the subject of perception (human sense organs or technical devices) to identity specific features of that persons look. In connection with this, the authors claim that a persons outward features are a complex system of elements that together form his or her appearance - an image that is visually perceived by other people or by devices. Taking into account the perceptibility of a persons outward look, his or her general physical features, besides the traditional ones, will include the geometry of hands, the vein structure on palm surfaces, finger thermogram, face and skull geometry, etc., all of which have a high criminalistic value. However, considering the current sanitary and epidemiology measures undertaken in Russia (wearing medical masks and gloves), climate (covering faces with scarves and kerchiefs) and cultural (women wearing national clothes that cover their faces, men with stubble or a beard) specifics, as well as the methods of falsifying anatomical features and counteracting criminalistic technologies already developed by the criminals, the authors view the research of the dynamic characteristics of a persons outward appearance in the form of digital behavior as the most perspective area. These characteristics primarily concern stylometry (keyboard handwriting) - a set of dynamic characteristics of pressing keys on a computer keyboard, which includes a system of subconscious automatic actions habitual to the user. Besides, the most relevant features of digital behavior should include digital handwriting, gestures, facial expression and lip movement.

Measuring the tilt and slant of Chinese handwriting in primary school students: A computerized approach.

Background“Horizontal strokes should be level and vertical strokes should be straight” is a common guideline in the teaching of Chinese handwriting. Measuring deviations in level horizontal and straight vertical strokes in students’ Chinese handwriting is usually assessed manually. However, this task is time-consuming and may have inconsistent outcomes when judged by different people. In this paper, we aim to formulate a method to automatically evaluate the tilt and slant degrees of students’ Chinese handwriting using digital handwriting tablets. Furthermore, we analyze the relationship between the tilt and slant features of students’ Chinese handwriting and other demographic and handwriting features.MethodsFive hundred and ninety-one primary school students from grades 1 to 6 were recruited in Hong Kong. Before the assessment, a grid paper was attached to a digital handwriting tablet. The participants were then asked to copy 90 Chinese characters from a template to the grid paper. Their handwriting processes were recorded as two-dimensional points and then analyzed. The tilt and slant of the students’ handwriting were calculated based on the inclination level of their horizontal and vertical strokes. Linear regressions between slant/tilt degree of the manuscripts and other handwriting features were performed. The students’ demographic information was also explored.ResultsSlant was found to be significantly correlated to Gender (p < 0.001) and tilt×standard deviation of pen pressure (p < 0.001). Tilt was found to be significantly correlated to ground time (p < 0.001), slant (p < 0.001) and slant×special education need (p = 0.021).ConclusionsOur results demonstrate the relationship between slant, tilt and Chinese handwriting performance in primary school children. Slant and tilt can be adopted as an indicator in students’ special education need diagnosis, as tilt level in the students’ Chinese handwriting was related to ground time and slant× special education need, while slant is related to tilt×standard deviation of pen pressure and female students. These findings may also inspire ways to increase special education need students’ writing speed.

THE APPLICATION OF GRAPHOLOGY AND ENNEAGRAM TECHNIQUES IN DETERMINING PERSONALITY TYPE BASED ON HANDWRITING FEATURES

This research was conducted with the aim of developing previous studies that have successfully applied the science of graphology to analyze digital handwriting and characteristics of his personality through shape based feature extraction, which in the present study will be applied one method of psychological tests commonly used by psychologists to recognize human’s personality that is Enneagram. The Enneagram method in principle will classify the personality traits of a person into nine types through a series of questions, which then calculated the amount of the overall weight of the answer. Thickness is what will provide direction personality type, which will then be matched with the personality type of the result of the graphology analysis of the handwriting. Personality type of handwritten analysis results is processed based on the personality traits that are the result of the identification of a combination of four dominant form of handwriting through the software output of previous studies, that Slant (tilt writing), Size (font size), Baseline, and Breaks (respite each word). From the results of this research can be found there is a correlation between personality analysis based on the psychology science to the graphology science, which results matching personality types by 81.6% of 49 respondents data who successfully tested.

Online Handwritten Gurmukhi Strokes Dataset Based on Minimal Set of Words

The online handwriting data are an integral part of data analysis and classification research, as collected handwritten data offers many challenges to group handwritten stroke classes. The present work has been done for grouping handwritten strokes from the Indic script Gurmukhi. Gurmukhi is the script of the popular and widely spoken language Punjabi. The present work includes development of the dataset of Gurmukhi words in the context of online handwriting recognition for real-life use applications, such as maps navigation. We have collected the data of 100 writers from the largest cities in the Punjab region. The writers’ variations, such as writing skill level (beginner, moderate, and expert), gender, right or left handedness, and their adaptability to digital handwriting, have been considered in dataset development. We have introduced a novel technique to form handwritten stroke classes based on a limited set of words. The presence of all alphabets including vowels of Gurmukhi script has been considered before selection of a word. The developed dataset includes 39,411 strokes from handwritten words and forms 72 classes of strokes after using a k-means clustering technique and manual verification through expert and moderate writers. We have achieved recognition results using the Hidden Markov Model as 87.10%, 85.43%, and 84.33% for middle zone strokes when using training data as 66%, 50%, and 80% of the developed dataset. The present work is a step in a direction to find groups for unknown handwriting strokes with reasonably higher levels of accuracy.

Constrained discriminant neighborhood embedding for high dimensional data feature extraction

When handling pattern classification problem such as face recognition and digital handwriting identification, image data is always represented to high dimensional vectors, from which discriminant features are extracted using dimensionality reduction methods. So in this paper, we present a supervised manifold learning based dimensionality reduction method named constrained discirminant neighborhood embedding (CDNE). In the proposed CDNE, on one hand, the class information of samples is taken into account to construct both an inter-class graph and an intra-class graph, where neighborhood points in the intra-class graph are selected from those with the same class and any point in the inter-class graph should sample those labeled different classes as its neighborhood points. On the other hand, locally least linear reconstruction technique is also introduced to model an objective function under the local uncorrelation constraint to explore a discriminant subspace. Compared to some related and state-of-the-art dimensionality reduction methods such as discriminant neighborhood embedding (DNE), supervised locality discriminant manifold learning (SLDML), discriminant sparse neighborhood preserving embedding (DSNPE), local graph embedding based on maximum margin criterion (LGE/MMC), uncorrelated discriminant locality preserving projection (UDLPP) and locally uncorrelated discriminant projection (LUDP), the proposed CDNE has been validated to be efficient and feasible by experimental results on some benchmark face data sets including CMU PIE, ORL and FERET.

Student Interaction with a Computer Tablet Exam Application Replicating the Traditional Paper Exam

The purpose of this study was to develop a computer tablet-based examination application (TabExam) and to explore its potential to replace the traditional paper-based exams. It was completed in digital handwriting with a stylus pen and submitted online for marking and feedback. Comparisons of student exam scores on both the TabExam and a parallel traditional paperbased exam indicated no difference, suggesting that the change in exam mode did not affect academic performance. While students reported that the TabExam had some advantages over paper exams, they had a neutral view on using it as a complete replacement for paper-based exams. More than half of the students accessed the online feedback provided after the TabExam while none of the students requested feedback on their paper exams. In addition to the quantitative data, this paper also provides a students’ perspective into aspects that they perceive as positive or negative, or need improvement, with regards to the use of tablets for examinations.

INNOVATIVE USE OF A TABLET DEVICE TO DELIVER INSTRUCTION IN UNDERGRADUATE CHEMISTRY LECTURES

This report describes a simple, inexpensive and highly effective instructional model based on the use of a tablet device to enable the real-time projection of the instructor's digitally handwritten annotations to teach chemistry in undergraduate courses. The projection of digital handwriting allows the instructor to build, present and adapt the class contents in a dynamic fashion and to save anything that is annotated or displayed on the screen for subsequent sharing with students after each session. This method avoids the loss of continuity and information that often occurs when instructors switch between electronic slides and white/chalk board during lessons. Students acknowledged that this methodology allows them to follow the instructor's cognitive process and the progressive development of contents during lectures as the most valuable aspect of the implemented instructional model.

Evolutionary compact embedding for large-scale image classification

Effective dimensionality reduction is a classical research area for many large-scale analysis tasks in computer vision. Several recent methods attempt to learn either graph embedding or binary hashing for fast and accurate applications. In this paper, we propose a novel framework to automatically learn the task-specific compact coding, called evolutionary compact embedding (ECE), which can be regarded as an optimization algorithm combining genetic programming (GP) and a boosting trick. As an evolutionary computation methodology, GP can solve problems inspired by natural evolution without any prior knowledge of the solutions. In our evolutionary architecture, each bit of ECE is iteratively computed using a binary classification function, which is generated through GP evolving by jointly minimizing its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimization leading to small Hamming distances for similar samples and large distances for dissimilar samples. We then evaluate ECE on four image datasets: USPS digital hand-writing, CMU PIE face, CIFAR-10 tiny image and SUN397 scene, showing the accurate and robust performance of our method for large-scale image classification.

Does Digital Handwriting of Instructors Using the iPad Enhance Student Learning?

Recently, projected electronic slides have been the most common tool used in classrooms. However, electronic projection slides, such as PowerPoint slides, do not provide sufficient flexibility to augment the displayed materials; therefore, instructors are not able to adjust their instruction instantly in response to the audience reaction and needs. Accordingly, many studies have suggested the use of tablet PC-based presentation tools in order to enable real-time handwritten annotations on predeveloped materials. Based on this rationale, the present study was designed in order to examine the effects of a tablet device (iPad in this study)-based instructors’ digital handwriting on students’ learning. Participants were 36 undergraduate students: half of the students were instructed using the typical PowerPoint-based presentation without the digital handwriting of the instructor, whereas the other half of the students were instructed using a tablet-based presentation with the instructor’s digital handwriting. Results from a MANCOVA revealed a significant main effect for the presentation mode. Two follow-up ANCOVAs revealed that students in the tablet-based instructor’s digital handwriting condition significantly outperformed the students in the animated PowerPoint-based presentation lecture for conceptual knowledge acquisition, despite the fact that there was no statistical difference in factual knowledge acquisition.

Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine

Handwriting analysis is a method to predict personality of an author and to better understand the writer. Allograph and allograph combination analysis is a scientific method of writer identification and evaluating the behavior. To make this computerized we considered six main different types of features: (i) size of letters, (ii) slant of letters and words, (iii) baseline, (iv) pen pressure, (v) spacing between letters and (vi) spacing between words in a document to identify the personality of the writer. Segmentation is used to calculate the features from digital handwriting and is trained to SVM which outputs the behavior of the writer. For this experiment 100 different writers were used for different handwriting data samples. The proposed method gives about 94% of accuracy rate with RBF kernel. In this paper an automatic method has been proposed to predict the psychological personality of the writer. The system performance is measured under two different conditions with the same sample.