Ramlan Siti Azura, Isa Iza Sazanita, Ismail Ahmad Puad, Osman Muhammad Khusairi, Soh Zainal Hisham Che
Electrical Engineering Studies, College of Engineering, Universiti Teknologi MARA, Cawangan Pulau Pinang, Permatang Pauh Campus, 13500 Permatang Pauh, Penang, Malaysia.
Data Brief. 2024 May 18;54:110534. doi: 10.1016/j.dib.2024.110534. eCollection 2024 Jun.
This report presents a dataset of offline handwriting samples among Malaysian schoolchildren with potential dysgraphia. The images contained Malay sentences written by primary school students and children under intervention by the Malaysia Dyslexia Association (PDM). Students were expected to copy and write the sentences provided on the paper form that was used to gather data. Students were required to write three sets of sentences. The paper was digitalized by scanning it and converting it into digital form. Furthermore, the images were pre-processed using image processing techniques by converting the images into binary format and interchanging the foreground and background colors. The images were then classified into two categories, namely potential dysgraphia and low potential dysgraphia. The dataset comprised a total of 249 handwriting images, obtained from a sample of 83 participants who were selected in the data collection process, with 114 for potential dysgraphia and 135 for low potential dysgraphia. Both categories of handwriting images were prepared in black and white images.
本报告展示了一组马来西亚学童潜在书写障碍的离线手写样本数据集。这些图像包含小学生以及马来西亚阅读障碍协会(PDM)干预下的儿童所书写的马来语句子。学生们需要抄写并书写在用于收集数据的纸质表格上提供的句子。学生们被要求书写三组句子。通过扫描纸张并将其转换为数字形式,纸张被数字化。此外,使用图像处理技术对图像进行预处理,将图像转换为二进制格式并交换前景和背景颜色。然后将图像分为两类,即潜在书写障碍和低潜在书写障碍。该数据集总共包含249张手写图像,这些图像来自数据收集过程中选取的83名参与者的样本,其中114张为潜在书写障碍图像,135张为低潜在书写障碍图像。两类手写图像均以黑白图像形式呈现。