The objective of this study was to compare the effect of computer-assisted practice with the sensorimotor approach on the remediation of handwriting problems in children with dysgraphia. In a randomized controlled trial, experiments were conducted to verify the intervention effect. Forty two children with handwriting deficit were assigned to computer-assisted instruction, sensorimotor training,...

The aim of the present study was to analyze handwriting difficulties in children with developmental coordination disorder (DCD) and investigate the hypothesis that a deficit in procedural learning could help to explain them. The experimental set-up was designed to compare the performances of children with DCD with those of a non-DCD group on tasks that rely on motor learning in different ways, ...

In this work a script independent handwriting recognition system is proposed which is derived from the RWTH-ASR hidden Markov model (HMM) based speech recognizer. Most problems occurring in handwriting recognition (HWR) are induced by large variations within the written text. In particular, different handwriting styles such as cursive writing or long drawn-out strokes are difficult to model. Co...

A hidden Markov model (HMM) based continuous speech recognition system is applied to on-line cursive handwriting recognition. The base system is unmodified except for using handwriting feature vectors instead of speech. Due to inherent properties of HMMs, segmentation of the handwritten script sentences is unnecessary. A 1.1% word error rate is achieved for a 3050 word lexicon, 52 character, wr...

Two studies were carried out in order to better understand the role of perceptual and visuo-motor skills in handwriting. Two training programs, visual-haptic (VH) and visual (V), were compared which differed in the way children explored the letters. The results revealed that improvements of VH training on letter recognition and handwriting quality were higher than improvements after V training....

In this paper we address the problem of the identification of text from noisy documents. We segment and identify handwriting from machine printed text because 1) handwriting in a document often indicates corrections, additions or other supplemental information that should be treated differently from the main or body content, and 2) the segmentation and recognition techniques for machine printed...

As handwriting is influenced by physiology, training, and other behavioral factors, a study of the handwriting of twins can shed light on the individuality of handwriting. This paper describes the methodology and results of such a study where handwriting samples of twins were compared by an automatic handwriting verification system. The results complement that of a previous study where a divers...

Handwriting is a natural and versatile method for humancomputer interaction, especially on small mobile devices such as smart phones. However, as handwriting varies significantly from person to person, it is difficult to design handwriting recognizers that perform well for all users. A natural solution is to use machine learning to adapt the recognizer to the user. One complicating factor is th...

Handwriting is a skill that is personal to individuals. The term “handwriting” is defined to mean as a surface consisting of artificial graphic marks conveying some message through the mark’s conventional relation to language (Plamondon & Srihari, 2000). Handwriting recognition is the task of producing the symbolic form, from the stored information of the handwriting data. Handwriting data is c...

Degenerative nerve diseases affect many of your body’s activities, such as balance, movement, talking, breathing, and heart function. These disease cannot be cured, nonetheless an early diagnosis can help to better manage the symptoms and the evolution of these diseases. Since handwriting involves several cognitive abilities, clinicians started to consider handwriting analysis as an effective t...