(untitled) (Academic Journal of International University of Erbil, 2025)
We introduce the Kurdish Handwritten Word Dataset (KHWD), this is the first large-scale collection of handwritten word images created from printed forms by 8,000 native Sorani Kurdish writers. The dataset contains around 400,000-word samples covering 10,000 unique words, captured via high-resolution scanning (1200 DPI) and processed through an automated segmentation pipeline to produce aligned image–transcription pairs (with CSV mappings). This work specifically highlights the challenges of the Sorani script (cursive joins, diacritics, dot-position distinctions, and right-to-left writing) that motivate our dataset design. We employ a Convolutional Recurrent Neural Network (CRNN) for recognition, evaluating multiple CNN backbones (ResNet18, ResNet50, GoogleNet, and MobileNetV2.The best results were achieved by MobileNetV2 pretrained on isolated Kurdish characters (achieving 97% training and 96% validation accuracy) and then fine-tuned on a combination of KHWD and synthetically generated word images, this augmentation yields significantly better performance The improved MobileNetV2 achieves approximately 17.98% CER, 54.31% WER, and 45.69% word-level accuracy, reducing CER by 1.47%, WER by 3.28%, and increasing word-level accuracy by 3.28% compared to the baseline MobileNetV2.