An Ensemble Transfer Learning Model for the Automatic Handwriting Recognition of Kurdish Letters - Qadir, Abdalbasit; Abdalla, Peshraw; Ghareb, Mazen; Abd, Dana; HamaKarim, Karwan (Iraqi Journal for Electrical and Electronic Engineering, 2025)
Automatic handwriting recognition is a fundamental component of various applications in various fields. During thelast three decades, it has become a challenging issue that has attracted much attention. Latin language handwritingrecognition has been the primary focus of researchers. As for the Kurdish language, only a few researches have beenconducted. This study uses a Kurdish character dataset, which contains 40,940 characters written by 390 native writers.We present an ensemble transfer learning-based model for automatically recognizing handwritten Kurdish letters usingDensenet-201, InceptionV3, Xception, and an ensemble of these pre-trained models. The model’s performance andresults obtained by the proposed ensemble model are promising, with a 97% accuracy rate, outperforming other studieson Kurdish character recognition.