Potential Dysgraphia Handwriting Dataset of School-Age Children

Dysgraphia is characterized as a learning disability in which a person lacks writing skills that are expected for his or her age and cognitive level. Despite this, phrases such as handwriting difficulties or delayed writing performance in children are used to indicate dysgraphia. It has an effect on both the handwriting product (legibility of the written trace) and the writing process (movement that generates the trace). The dysgraphia dataset is an image-shaped dataset that contains images of children's writings that may be dysgraphia-related. These datasets were gathered through data collection techniques carried out by primary school students and children undertaking interventions at the Malaysian Dyslexia Association (PDM). Two PDM authorities then evaluated the dataset to determine whether the handwriting had potential dysgraphia or low potential dysgraphia. This dataset is used to determine the risk of dysgraphia in children. Machine learning techniques can be used to implement this data. This dataset has two types of dysgraphia levels: potential dysgraphia and low potential dysgraphia. Through the geometric characteristics and features of the writing in the image, this dataset could potentially be utilized to determine the presence of dysgraphia symptoms.

书写困难症（Dysgraphia）是一种学习障碍，表现为个体在年龄和认知水平上缺乏预期的书写技能。尽管如此，诸如书写困难或儿童写作表现的延迟等短语，常被用来指代书写困难症。该病症对书写成果（书写痕迹的易读性）以及书写过程（生成痕迹的运动）均有影响。书写困难症数据集是一种图像型数据集，其中包含了可能与书写困难症相关的儿童书写图像。这些数据集是通过马来西亚读写障碍协会（PDM）的初级学校学生和接受干预的儿童所进行的数据收集技术搜集而来的。随后，两名PDM权威人士对该数据集进行了评估，以确定书写是否具有潜在的书写困难症或低潜在书写困难症。本数据集旨在评估儿童书写困难症的风险。可利用机器学习技术对该数据进行实现。本数据集包含两种书写困难症级别：潜在书写困难症和低潜在书写困难症。通过图像中书写的手法几何特征和特性，本数据集有望被用于确定书写困难症症状的存在。