Graphics Tablet Research Articles

The role of graphics tablets in the development of artistic and creative abilities of preschool children in multicultural education

ABSTRACT The development of graphic skills in preschool children is of great significance for their overall development, as well as for the enhancement of attentiveness and coordination abilities. The objective of this study is to examine methods for fostering artistic and creative skills among students within the framework of multicultural education through the use of graphics tablets. The authors conducted a four-month training program focused on drawing skills using graphics tablets, which involved studying the culture of China, Asian, and European countries through various art forms, as well as the basics of color science. Upon completion of the course, the authors found that 89% of children and 84% of parents responded positively to this type of training, as it allowed the children to effectively master drawing. However, there were certain technical skills gaps, such as a lack of accuracy in drawing angles, too large or too small drawings, and a lack of accuracy in drawing lines to given points. These gaps were eliminated as a result of studying additionally developed recommendations. The practical significance of this study lies in the search for effective mechanisms to develop drawing skills using graphics tablets in preschool education.

Digital Artistry: Exploring Techniques and Trends in Indian Art and Culture

From prehistoric cave paintings to contemporary digital creations, art has been integral to human expression throughout history. Digital art in modern art gradually includes various forms, such as Painting, sculpture and animation, using technology as its medium. Digital art includes any artistic work using digital technology, including Painting, 3D modelling, and interactive installations. It combines human creativity with computer technology, expanding the possibilities of creative expression. Studying digital artistry in India provides insights into cultural preservation, technological innovation, and social commentary. It promotes educational development and economic opportunities, contributing to the country's vibrant artistic scene. Technological advancements have democratized digital art creation in India, leading to diverse artistic expressions. Integrating digital art with emerging technologies like AR, VR, and NFTs opens up new possibilities for artistic exploration and dissemination. Digital art techniques include digital Painting, vector art, and 3D modelling, facilitated by hardware such as graphics tablets and software such as Adobe Creative Cloud. Brushes, textures and plugins enhance the creative process, while photo manipulation and 3D sculpting provide new avenues for artistic expression. Digital artistry in India offers insights into cultural dynamism, technological innovation and artistic development. This research paper delves into the evolving landscape of digital artistry in India, exploring various techniques, trends, and the socio-cultural impact of digital art in the country.

Explainability of CNN-based Alzheimer's disease detection from online handwriting.

With over 55 million people globally affected by dementia and nearly 10 million new cases reported annually, Alzheimer's disease is a prevalent and challenging neurodegenerative disorder. Despite significant advancements in machine learning techniques for Alzheimer's disease detection, the widespread adoption of deep learning models raises concerns about their explainability. The lack of explainability in deep learning models for online handwriting analysis is a critical gap in the literature in the context of Alzheimer's disease detection. This paper addresses this challenge by interpreting predictions from a Convolutional Neural Network applied to multivariate time series data, generated by online handwriting data associated with continuous loop series handwritten on a graphical tablet. Our explainability methods reveal distinct motor behavior characteristics for healthy individuals and those diagnosed with Alzheimer's. Healthy subjects exhibited consistent, smooth movements, while Alzheimer's patients demonstrated erratic patterns marked by abrupt stops and direction changes. This emphasizes the critical role of explainability in translating complex models into clinically relevant insights. Our research contributes to the enhancement of early diagnosis, providing significant and reliable insights to stakeholders involved in patient care and intervention strategies. Our work bridges the gap between machine learning predictions and clinical insights, fostering a more effective and understandable application of advanced models for Alzheimer's disease assessment.

Acute stress does not influence the learning of a precise manual task: A randomized clinical trial

Acute stress is frequent in sports and rehabilitation contexts and can impact cognitive processes essential for motor learning. This study aimed to investigate the influence of induced acute stress on the learning of a precise manual task, examining its effect on five key parameters of fine motor control: trajectory error, trajectory error direction, time error, tracing accuracy, and task accuracy. A double-masked, randomized clinical trial with 62 participants (average age 20.65 ± 2.54 years; 39 females; 23 males) was conducted. To examine the effects of stress, participants were assigned to either a stress or a control group through stratified randomization by sex. Initially, all participants underwent the Maastricht Acute Stress Test (in its acute stress and control versions, respectively). Subsequently, they performed the precise manual task on a graphic tablet at three stages of the learning process: acquisition, short-term retrieval, and long-term retrieval. Electrodermal activity and heart rate variability were recorded to assess stress induction. Data analysis from 30 stress group participants and 25 control group participants revealed no statistically significant differences between groups in any of the variables studied at the three learning stages. Both groups exhibited statistically significant improvements in time error, trajectory error direction, and tracing accuracy during both short-term and long-term retrieval compared to acquisition. Our findings suggest that acute physical and psychological stress does not markedly impair learning a precise manual task of adhering to a specific trajectory and pace among young adults.

Evaluating Neural Network Models For Predicting Dynamic Signature Signals

Abstract A signature is a biometric attribute commonly used for identity verification. It can be represented by a shape created with a classic pen, but it can also contain dynamic information. This information is acquired using a digital input device, such as a graphic tablet or a digital screen and stylus. Information about the dynamics of the signing process is stored in the form of signals that change over time, including pen velocity, pressure, and more. These dynamics are characteristic of an individual and are difficult for a human to forge. However, it is an interesting research issue whether the values of signals describing a dynamic signature can be predicted using artificial intelligence methods. Predicting the dynamics of the signals describing a signature would benefit various scientific problems, including improving the quality of reference signals by detecting anomalies, creating signature templates better suited to individuals, and more effectively detecting potential forgeries by identity verification systems. In this paper, we propose a method for predicting dynamic signature signals using an artificial neural network. The method was evaluated using samples collected in the DeepSignDB database, distributed by BiDA Lab.

Optimal evolutionary framework-based activation function for image classification

Typically, supervised Machine Learning (ML)-based image classifiers leverage algorithms derived from either Artificial Neural Networks (ANNs) or optimal separating hyperplane (OSH)-based algorithms. However, despite recent progress has been made to enhance ANNs’ classification performance via the Rectified Linear Unit (ReLU)-based activation functions (AFs), there is currently no AF that scales across and benefit both ANNs and OSH-based classifiers. Moreover, the lack of globally optimal AFs leads to a high variance in image classification-related results. Thus, this study seeks to overcome this limitation by implementing a next-generation evolutionary framework (‘ActiGen’) to generate a novel and more reliable AF, which can scale to two families of AFs for two classifiers. The proposed evolutionary knowledge-based framework leverages a Multi-Objective (MO) optimisation method based on Genetic Algorithms (GA), or ‘MOGA’, to improve the generalisation of such classifiers. This evolutionary framework and its generated AF are validated using nine open-access datasets: seven image-based datasets, consisting of 22,136 images in total, and two large (561 features for 10,929 instances, 124 features for 1,700 instances) tabular datasets. These diverse datasets include both binary and multi-class classification, such as images of breast masses, those acquired via cardiac computed tomography, photos of famous people from the Internet, images of handwritten digits and those drawn on a graphics tablet, human faces with different lighting, details, and expressions, smartphone-related data captured during various activities and postural transitions, and clinical data on complications of myocardial infarction. Findings demonstrate that the proposed evolutionary optimisation framework (‘ActiGen-MOGA’) was able to generate a novel scalable AF, which led to achieve the highest classification performance and the fastest convergence across six out of nine datasets. In the best classification task, the ActiGen-MOGA-based AF led to a classification performance of 80 % and 78 % higher than the polynomial and Rectified Linear Unit (ReLU) AFs respectively.

Dysgraphia Differs Between Children With Developmental Coordination Disorder and/or Reading Disorder

Handwriting deficits, or dysgraphia, are present in several neurodevelopmental disorders. To investigate whether dysgraphia differs according to the associated disorder, we performed a detailed analysis of handwriting in a group of French children with developmental coordination disorders (DCD), reading disorder (RD), or comorbid RD and DCD. Handwriting deficits were investigated at the product (quality of the trace) and the process (movement that generates the trace) levels. Nineteen children with singular RD (among which eight with dysgraphia), 13 children with singular DCD (among which seven with dysgraphia), 16 children with comorbid RD+DCD (among which 11 with dysgraphia), and 20 typically developing children, age 7 to 12, performed the BHK (Brave Handwriting Kinder) test, a standardized assessment of handwriting, on a graphic tablet. Developmental coordination disorders primarily affected handwriting quality, while RD affected slowness and, to a lesser extent, quality. Children with RD, solely or comorbid with DCD, wasted time by lifting and stopping the pen when writing. The comorbidity added to but did not worsen, handwriting difficulties. These results reflect distinct motor impairments and/or strategies in children with DCD or RD. We identified subtypes of dysgraphia and advocated for a fine-grained analysis of the writing process and the assessment of motor and reading skills when studying dysgraphia.

Students’ Perception of Interactive Progressive Drawing Using a Graphic Tablet in Histology Education: A Mixed-Method Evaluation Study

Abstract Background: It is important that relevant diagrams that are clear and visible are drawn by the teacher of histology for a better understanding of the subject. It is also desirable that the student see the buildup of the diagram. To achieve the same in an electronic format, a graphic tablet was used for interactive progressive drawing (IPD) in histology education. Aim and Objective: To assess students’ perception of usefulness and effectiveness of IPD, in histology education. Methodology: Students were taught histology by IPD, using a graphic tablet in a classroom setting. Their perception about the method of teaching and the quality of the diagrams was obtained by an online questionnaire having open-ended and closed-ended questions. The mean and standard deviation of ratings on the Likert scale about student perception were calculated. The percentage of students who gave a rating of 4 out of 5 or above for the questions was measured as an indicator of satisfaction of the students. The answers to the open-ended questions were categorized and analyzed by the grounded theory approach. Results: A total of 137 (54.8%) students responded to the questionnaire. Seventy-eight percent of students preferred the diagrams drawn such over textbook illustrations and perceived the innovation to be helpful in improving basic understanding on a scale of 4/5 or above. Conclusion: Teaching histology by IPD using a graphic tablet was perceived by students to be better than teaching using a Power Point presentation.

Contributions of remote collaborative sketching to the design of infovis in public health

This paper presents contributions from using remote collaborative sketching to infovis for public health. The results come from an interdisciplinary study that involved designers, health researchers, public managers and computer scientists in the production of sketches generating alternatives for creating valuable graphics and dashboards. One of the creative stages was the remote collaborative workshops in which design alternatives were proposed based on sketches. The sketches were developed with the support of videoconferencing meetings, virtual boards, and graphic tablets. From this practice, we were able to include as contributions to the creative process in infovis: remote collaboration expansion; agile and iterative cycles improvement; interface design preview; data modeling supporting; addressing graphic literacy issues; creative process documentation; participant experience improvement; and additional information incorporation.

Handwriting processes when spelling morphologically complex words in children with and without Developmental Language Disorder.

Representations activated during handwriting production code information on morphological structure and reflect decomposition of the root and suffix. Children with Developmental Language Disorder (DLD) have significant difficulties in spelling morphologically complex words, but previous research has not sought evidence for a morphological decomposition effect via an examination of handwriting processes in this population. Thirty-three children aged 9-10 years with DLD, 33 children matched for chronological age (CA), and 33 younger children aged 7-8 years matched for oral language ability (LA) completed a dictated spelling task (21 words; 12 with inflectional suffixes, nine with derivational suffixes). The task was completed on paper with an inking pen linked to a graphics tablet running the handwriting software Eye and Pen. Pause analyses and letter duration analyses were conducted. The three groups showed similar handwriting processes, evidencing a morphological decomposition effect in a natural writing task. Pause durations observed at the root/suffix boundary were significantly longer than those occurring in the root. Letter durations were also significantly longer for the letter immediately prior to the boundary compared to the letter after it. Nevertheless, despite being commensurate to their LA matches for mean pause durations and letter durations, children with DLD were significantly poorer at spelling derivational morphemes. Handwriting processes did significantly predict spelling accuracy but to a much lesser extent compared to reading ability. It is suggested that derivational spelling difficulties in DLD may derive more from problems with underspecified orthographic representations as opposed to handwriting processing differences.

A Spatio-Temporal Siamese Neural Network for Multimodal Handwriting Abnormality Screening of Parkinson’s Disease

Currently, hand motion recognition of single-modality data has been extensively explored for the analysis of various contact and noncontact sensors, and it is recognized that all the existing technologies have both strengths and limitations. As a significant motor symptom, hand tremor is usually utilized for the diagnosis and evaluation of Parkinson’s disease; furthermore, a multimodal analysis of the handwriting pattern of the patient has made up for the one-sided way of learning the hand movement in a single measurement dimension. Especially, considering a variety of measurement resources, it shows promising performance in recognizing handwriting patterns of Parkinson’s disease. In this work, a novel Spatio-temporal Siamese neural network (ST-SiamNN) is proposed to learn the handwriting differences between healthy individuals and patients with Parkinson’s disease, process data onto multiple sensors, and enhance the characteristics of handwriting in Parkinson’s disease. Uniquely, it is a discriminative model of multilabel and multinetwork constructed by a Siamese network, which consists of four modules: a preprocessor for handwritten data enhancement, a Siamese bidirectional memory neural network (SiamBiMNN) for temporal and texture feature extraction and difference enhancement, a Siamese octave convolutional neural network (SiamOctCNN) for spatial feature extraction and difference enhancement, and a decision-making layer to rejudge the output features of the Siamese networks to obtain more accurate auxiliary diagnosis results. The framework proposed in this article is verified on two handwritten datasets of multiple modalities, i.e., images, smart pen signals, and graphics tablet signals, which are compared with several state-of-the-art studies.

Impaired Metacognition of Voluntary Movement in Functional Movement Disorder.

Motor symptoms in functional movement disorders (FMDs) are experienced as involuntary but share characteristics of voluntary action. Clinical and experimental evidence indicate alterations in monitoring, control, and subjective experience of self-performed movements. The objective of this study was to test the prediction that FMDs are associated with a reduced ability to make accurate (metacognitive) judgments about self-performed movements. We compared 24 patients with FMD (including functional gait disturbance, functional tremor, and functional tics) with 24 age- and sex-matched healthy control subjects in a novel visuomotor-metacognitive paradigm. Participants performed target-directed movements on a graphics tablet with restricted visual feedback, decided which of two visually presented trajectories was closer to their preceding movement, and reported their confidence in the visuomotor decision. We quantified individual metacognitive performance as participants' ability to assign high confidence preferentially to correct visuomotor decisions. Patients and control subjects showed comparable motor performance, response accuracy, and use of the confidence scale. However, visuomotor sensitivity in the trajectory judgment was reduced in patients with FMD compared with healthy control subjects. Moreover, metacognitive performance was impaired in patients, that is, their confidence ratings were less predictive of the correctness of visuomotor decisions. Exploratory subgroup analyses suggest metacognitive deficits to be most pronounced in patients with a functional gait disturbance or functional tremor. Patients with FMD exhibited deficits both when making visuomotor decisions about their own movements and in the metacognitive evaluation of these decisions. Reduced metacognitive insight into voluntary motor control may play a role in FMD pathophysiology and could lay the groundwork for new treatment strategies. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A new test to detect impairments of sequential visuospatial memory due to lesions of the temporal lobe.

This study investigates visuospatial memory in patients with unilateral lesions of the temporal lobe and the hippocampus resulting from surgery to treat drug-resistant epilepsy. To detect impairments of visuospatial memory in these individuals, a memory test should be specific to episodic memory, the type of memory in which the hippocampus is crucially involved. However, most known visuospatial memory tests do not focus on episodic memory. We hypothesized that a new sequential visuospatial memory test, which has been previously developed and applied only in healthy subjects, might be suitable to fill this gap. The test requires the subject to reproduce a memorized sequence of target locations in ordered recall by typing on a blank graphics tablet. The length of the memorized sequence extended successively after repeated presentation of a sequence of 20 target positions. The test was done twice on day one and again after one week. Visual working memory was tested with the Corsi block-tapping task. The performance in the new test was also related to the performance of the patients in the standard test battery of the neuropsychological examination in the clinical context. Thirteen patients and 14 controls participated. Patients showed reduced learning speed in the new sequential visuospatial memory task. Right-sided lesions induced stronger impairments than left-sided lesions. After one week, retention was reduced in the patients with left-sided lesions. The performance of the patients in commonly used tests of the neuropsychological standard battery did not differ compared to healthy subjects, whereas the new test allowed discrimination between patients and controls at a high correct-decision rate of 0.89. The Corsi block-span of the patients was slightly shorter than that of the controls. The results suggest that the new test provides a specific investigation of episodic visuospatial memory. Hemispheric asymmetries were consistent with the general hypothesis of right hemispheric dominance in visuospatial processing.

CHARACTERISTICS OF ARABIC HANDWRITING ON GRAPHIC TABLET IN NEURODEGENERATIVE DISEASE: PRELIMINARY RESULTS BETWEEN PATIENTS WITH ALZHEIMER’S DISEASE AND HEALTHY CONTROLS

Handwriting is a component of the complex language that came about late in the history of mankind and which develops late in human beings. Numerous works have raised changes in both the graphic and kinematic characteristics of writing. Although, age does not modify the lexical and syntactic parameters of language, it can however modify its spatial structure, especially pressure and speed. Many neurodegenerative pathologies, especially Alzheimer's disease, are characterized by a progressive disorganization of writing. Depending on the cognitive stage of the dementia, the graphic gesture deteriorates as does the spatial construction. Objective: Our study aims at assessing the characteristics of Arabic writing in a healthy Moroccan population and to compare it to people with mild to moderate Alzheimer's disease. Our objective is to help health professionals detect early cognitive deterioration in neurodegenerative diseases by analyzing the graphic gesture. Handwriting is captured on a graphic tablet (WACOM) and is analyzed "online" as a sequence of acquired signals (position, pressure, speed and pen inclination) in Moroccan patients with mild to moderate Alzheimer's disease and these were compared to those of normal volunteers. We performed a first analysis of the results from 18 Alzheimer's patients compared to 18 control subjects. The results reveal differences between the control and Alzheimer's groups. AD subjects had lower speeds and accelerations compared to the control subjects. The time spent on paper and in the air was significantly greater in the AD subjects. This preliminary analysis of the results allowed us to identify distinguishing characteristics through the analysis of different handwriting parameters in order to identify the two groups studied.

Deficient Interhemispheric Connectivity Underlies Movement Irregularities in Parkinson’s Disease

Movement execution is impaired in patients with Parkinson's disease. Evolving neurodegeneration leads to altered connectivity between distinct regions of the brain and altered activity at interconnected areas. How connectivity alterations influence complex movements like drawing spirals in Parkinson's disease patients remains largely unexplored. We investigated whether deteriorations in interregional connectivity relate to impaired execution of drawing. Twenty-nine patients and 31 age-matched healthy control participants drew spirals with both hands on a digital graphics tablet, and the regularity of drawing execution was evaluated by sample entropy. We recorded resting-state fMRI and task-related EEG, and calculated the time-resolved partial directed coherence to estimate effective connectivity for both imaging modalities to determine the extent and directionality of interregional interactions. Movement performance in Parkinson's disease patients was characterized by increased sample entropy, corresponding to enhanced irregularities in task execution. Effective connectivity between the motor cortices of both hemispheres, derived from resting-state fMRI, was significantly reduced in Parkinson's disease patients in comparison to controls. The connectivity strength in the nondominant to dominant hemisphere direction in both modalities was inversely correlated with irregularities during drawing, but not with the clinical state. Our findings suggest that interhemispheric connections are affected both at rest and during drawing movements by Parkinson's disease. This provides novel evidence that disruptions of interhemispheric information exchange play a pivotal role for impairments of complex movement execution in Parkinson's disease patients.

Analysis of postures for handwriting on touch screens without using tools

The act of handwriting affected the evolutionary development of humans and still impacts the motor cognition of individuals. However, the ubiquitous use of digital technologies has drastically decreased the number of times we really need to pick a pen up and write on paper. Nonetheless, the positive cognitive impact of handwriting is widely recognized, and a possible way to merge the benefits of handwriting and digital writing is to use suitable tools to write over touchscreens or graphics tablets. In this manuscript, we focus on the possibility of using the hand itself as a writing tool. A novel hand posture named FingerPen is introduced, and can be seen as a grasp performed by the hand on the index finger. A comparison with the most common posture that people tend to assume (i.e. index finger-only exploitation) is carried out by means of a biomechanical model. A conducted user study shows that the FingerPen is appreciated by users and leads to accurate writing traits.

Design and analysis of an electric bike chassis

Development of any vehicle, be it a two wheeler or a four wheeler, is a complex and detailed engineering process. For its progress, designers have to prepare concept sketches on 2D platforms (on sheets and graphic tablets), model them on 3D softwares and carry out a bunch of simulations and tests on various computer aided engineering softwares. The aim of this paper is to design and analyze an electric bike chassis and hence prove its stability by performing static structural test. Solidworks was used for modeling the chassis and ANSYS was utilized to analyze it. The frame presented in this study is an original concept and dissimilar to the conventional IC engine chassis. Aspects like the weight of the chassis, its strength, rigidness, and storage space available to the consumer were considered while designing it. Initially, three different frames were modeled and after brainstorming session with experts, the best one out of the three was finalized considering all the above mentioned parameters in mind. In total, four materials were applied on the skeleton for its analysis. These included carbon steel AISI 1018, alloy steel AISI 4130, aluminum alloy 2018, and aluminum alloy AISI 7068 T6. The simulations were completed in true scale. Later on the results were compared and the deformations were thoroughly revised by the team. Considering the cost, weight and the final results of the deformations, the best material was picked up.

Examination of technology acceptance and TPACK competencies of mathematics teachers who are involved in distance education practices during the pandemic process

Distance education has become an important opportunity for teachers who do not use technology or use it little in learning environments to use ICT tools such as graphic tablets, interactive content and dynamic geometry software by requiring them to perform their courses with technological tools. In this regard, the present study aims to examine the technology acceptance and TPACK competencies of mathematics teachers in the distance education process that has been put into practice due to the impact of the pandemic on a global scale. In the study, exploratory correlational and causal-comparative research models were used. The sample of the study consists of 223 teachers selected from the mathematics teachers who perform mathematics education practices through distance education with criterion sampling, one of the purposive sampling methods. The personal information form, Technology Acceptance Scale for Teachers and TPACK Scale were used as data collection tools. In order to examine whether the technology acceptance scores and TPACK levels differed according to various variables, the independent t-test and the analysis of variance (one-way ANOVA) were applied.

Proximal and distal movement patterns during a graphomotor task in typically developing children and children with handwriting problems

BackgroundTherapists specializing in handwriting difficulties in children often address motor problems including both proximal and distal movements in the upper extremity. Kinematic measures can be used to investigate various aspects of handwriting. This study examined differences in movement patterns in proximal and distal joints of the upper extremity during graphomotor tasks between typically developing children with and without handwriting problems. Additionally, it explored relationships between movement patterns, speed, and legibility of writing.MethodsForty-one children, aged 7–11 years, were assessed with the Aleph Aleph Ktav Yad Hebrew Handwriting assessment and the Beery Test of Visual Motor Integration and, based on their scores, were divided into a research group (with handwriting difficulties) and a control group (without handwriting difficulties). Upper extremity joint movement patterns were analyzed with a motion capture system. Differences in the quality of shapes traced and copied on a graphics tablet positioned horizontally and vertically were compared. Between-group differences and relationships with speed and legibility were analyzed.ResultsIn both groups, there was greater movement in the distal compared to the proximal joints, greater movement when performing the task in a horizontal compared to a vertical plane, and greater movement when tracing than copying. Joint movements in the arm executed scaled-down versions of the shapes being drawn. While the amount of joint displacement was similar between groups, children in the research group showed greater dissimilarity between the drawn shape and the shape produced by the proximal joints. Finally, the drawing measure on the tablet was a significant predictor of legibility, speed of writing, visual motor integration and motor coordination, whereas the dissimilarity measure of joint movement was a significant predictor of speed of writing and motor coordination.ConclusionsThis study provides support for the role of the distal upper extremity joints in the writing process and some guidance to assist clinicians in devising treatment strategies for movement-related handwriting problems. While we observed differences in proximal joint movements between the children with and without handwriting difficulties, the extent to which they are responsible for the differences in drawing quality remains to be determined. Further studies should use a similar methodology to examine additional tasks such as drawing shapes of varying sizes.

Arabic Handwritten Recognition by using His Features

The present study is a contribution in the filed of Arabic character recognition. The first step is to enter an image from a scanner, and then some special processes are done on the image like (smoothing and thinning and resizing) before continuous to segmentation phase. Then modified moment invariant computed using the shape boundary of the character only is utilized as features.Also this study is concerned with the recognition of handwritten Arabic character drawn on a graphic tablet. At first feature that are suitable for recognition are proposed. Feature that are found to be independent of the writer style are represented as a list (vector) of integer values.The designed system in general view is an Arabic text recognition system basically includes a segmentation stage in order to recognize handwritten Arabic words. The segmentation process is completely based on tracing the outer of given word and calculating the output distance between the extreme points of intersection of the contour with vertical line. At the output of the segmentation stage the cursive word is presented as a sequence of isolated character contour.