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The oracle bone character (OBC) from ancient China is the most famous ancient writing systems around the world. Identifying and deciphering OBCs is one of the most important topics in oracle bone study. In research, one of the challenges is that the literature review usually leads to a huge cost of time and manpower. Therefore, the digitazation of OBC literature through the automatic recognition is the inevitable trend of future development. However, the OBCs in the literature are usually writing characters while the database of handwriting OBC has not yet been presented. In this paper, we establish a handwriting oracle bone character database called HWOBC, containing 83,245 character-level samples which are grouped into 3881-character categories. We also present the performance of several baseline DCNN-based methods, in which Melnyk-Net exhibits the best accuracy of 97.64%. It is anticipated that the publication of this database will facilitate the development of OBC research.

\"For the first time, the dramatic changes the Qur'anic code underwent during the Umayyad period (660-750 C.E.) are analysed and presented on the basis of a selection of material in good part unpublished. In 'Qur'ans of the Umayyads', François Déroche offers a chronology of the various developments which marked the period, in an approach combining philology, art history, codicology and palaeography. The conclusions he reaches challenge the traditional account about the writing down of the Qur'an and throw a new light on the role of the Umayyads in its handwritten diffusion.\" -- Back cover.

Dysgraphia, a disorder affecting the written expression of symbols and words, negatively impacts the academic results of pupils as well as their overall well-being. The use of automated procedures can make dysgraphia testing available to larger populations, thereby facilitating early intervention for those who need it. In this paper, we employed a machine learning approach to identify handwriting deteriorated by dysgraphia. To achieve this goal, we collected a new handwriting dataset consisting of several handwriting tasks and extracted a broad range of features to capture different aspects of handwriting. These were fed to a machine learning algorithm to predict whether handwriting is affected by dysgraphia. We compared several machine learning algorithms and discovered that the best results were achieved by the adaptive boosting (AdaBoost) algorithm. The results show that machine learning can be used to detect dysgraphia with almost 80% accuracy, even when dealing with a heterogeneous set of subjects differing in age, sex and handedness.

Acquiring writing skills is a long developmental process that is conditioned by both the mastery of the gesture and the spatio-temporal arrangement of characters across the page. While the researches in the literature mainly focused on spatio-temporal and kinematics parameters of tracing letters or words using digitizing tablets, no recent research has previously studied the developmental prerequisites of the organization of handwriting useful for clinical assessment and remediation. Aims of the present study was to investigate and validate the phenotyping of the developmental genesis of pre-scriptural graphomotor gestures among school-aged children in achieving correct handwriting. The subject was examined in depth in an ecological setting similar to school, with the objective of assessing handwriting developmental levels. The pre-scriptural graphomotor task studied was to copy a line of cycloid loops on a paper sheet put on the table. This task was chosen because it reflects the execution of the hand movements from one end of the line to the other and in an anti-clockwise direction, as in handwriting. A new methodological approach was applied incorporating both the maturative evolution of postural-gestural features (video-recorded for analysis in 2D reconstruction) and spatio-temporal/kinematic measures collected with a digital pen connected to an analysis software tool to assess the developmental level and provide an understanding of the phenotypical features of the graphomotor gesture. And we also evidence the concurrent validity of the data in displacements, and the better are the spatio-temporal and kinematic measures. Consequently there are phenotypical features, both postural-gestural and spatio-temporal/kinematic in the developmental genesis of the graphomotor gesture with an easy pre-scriptural task. Typically developing school children from 1st to 5th grade, was collected from elementary schools. Five main patterns of displacement gestures were found for the production of the line of loops with a significant developmental progress from 1st to 5th grade. In addition, significant results in comparisons with spatio-temporal and kinematic age-related normative data were highlighted, associated with the quality of the coordination gesture. Lastly external validity in relation to normative values with the standardized handwriting scale BHK (French adaptation of the Concise Evaluation Scale for Children's handwriting) showed certain significant correlations with spatio-temporal and kinematic measures and the evolution of the displacement gestures (five patterns) used to draw the loops. The better the motor control of the handwriting gesture, the less variety there is in inter-segmental and joint-scriptural task, enabling handwriting developmental levels to be assessed in screening for handwriting disorders, possibly co-occurring with other learning disabilities, and also useful in clinical decision-making processes for handwriting remediation, or simply to assist handwriting gesture acquisition in elementary school.

Abstract The research relevance is conditioned upon the lack of assistance tooling for forensics experts and graphologists that allow to justify examination and analysis results. It is a non-trivial task to develop such toolset. The goal of the research is to formalize handwriting features extraction process in order to validate an authorship of a given Russian handwritten text. Existing approaches analysis and a mathematical model of handwriting features extraction from handwritten documents are provided along with the choice of implementation approach. Software toolset that allows the model’s practical application is developed.

This paper proposes new ways to assess handwriting, a critical skill in any child's school journey. Traditionally, a pen and paper test called the BHK test (Concise Evaluation Scale for Children's Handwriting) is used to assess children's handwriting in French-speaking countries. Any child with a BHK score above a certain threshold is diagnosed as 'dysgraphic', meaning that they are then eligible for financial coverage for therapeutic support. We previously developed a version of the BHK for tablet computers which provides rich data on the dynamics of writing (acceleration, pressure, and so forth). The underlying model was trained on dysgraphic and non-dysgraphic children. In this contribution, we deviate from the original BHK for three reasons. First, in this instance, we are interested not in a binary output but rather a scale of handwriting difficulties, from the lightest cases to the most severe. Therefore, we wish to compute how far a child's score is from the average score of children of the same age and gender. Second, our model analyses dynamic features that are not accessible on paper; hence, the BHK is useful in this instance. Using the PCA (Principal Component Analysis) reduced the set of 53 handwriting features to three dimensions that are independent of the BHK. Nonetheless, we double-checked that, when clustering our data set along any of these three axes, we accurately detected dysgraphic children. Third, dysgraphia is an umbrella concept that embraces a broad variety of handwriting difficulties. Two children with the same global score can have totally different types of handwriting difficulties. For instance, one child could apply uneven pen pressure while another one could have trouble controlling their writing speed. Our new test not only provides a global score, but it also includes four specific score for kinematics, pressure, pen tilt and static features (letter shape). Replacing a global score with a more detailed profile enables the selection of remediation games that are very specific to each profile.

Manuscripta Biblica is dedicated to scholarship on manuscripts of the Jewish and Christian Bible. The series is open to all fields and methods that address biblical manuscripts in a broad sense. This includes research on textual and paratextual aspects, the presentation, organization, physical composition, and artistic dimensions of the artifact as well as issues related to production, dissemination, forms of use, and reception.

Writing is one of the most important medium for communication which can represent in one of the easiest ways like symbols and sign. Handwriting is one of the most complex activities in human motions which results in major disorders. Handwriting disorder is one of the major problems in humans and hence it should be rectified before leading into major disorders like dyspraxia, dysgraphia and others. This work is a step to identify the writing disorder in early stage by using EMG sensor. It consists of EMG -sensor which measures the muscle potential from two important muscles extensor digitorum and flexor carpi radialis. The electrodes are placed in such a way that it pick up the muscle potential. The extensor digitorum helps in movements of the elbow and wrist where we obtain minimum potential compared to flexor carpi radialis which is just the flexor of the wrist only. So extensor digitorum is the most prominent muscle where we obtain maximum potential because it extents the metacarpophalangeal joint. The prototype is tested against people of two aged groups, one with people aged above 50 years and the other with adult age between 16-30 years including both the genders. They are allowed to write in their own speed of three types slow, normal, fast, of standard sentence at one stretch to obtain better muscle potential without any deviation. The acquired muscle potential shows good results about muscle potential with respect to age while writing. This prototype can be extended to the early diagnosis of handwriting disorder which helps to rectify the neurogenerative disorder before leading into severe complications.

Abstract As technology develops, dictionaries have changed from paper to electronic. Electronic dictionaries can be an alternative medium for language learning because they combine sound, visuals and text that make learning foreign languages easier. One example of using an electronic dictionary is in reading subjects. Users can learn about how to pronounce a vocabulary and sound tones correctly by utilizing the audio feature in an electronic dictionary. Vocabulary recall through mobile device applications is far more effective than through other media. The electronic dictionary application that is widely used by Chinese Literature students in learning Mandarin is the Pleco and Google Translate application. Therefore, the authors are interested in making comparisons of the similarities and differences between the two applications for the recommendation of Mandarin learning aids. This study uses a comparison method by comparing the functions of the two e-dictionaries. From the results of the comparison, both applications have keyboard features, handwriting features, voice record features and scanning features for translating words. The difference is in the radical features, stroke, number words and sentences sample