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Handwritten character recognition (HCR) involves identifying characters in images, documents, and various sources such as forms surveys, questionnaires, and signatures, and transforming them into a machine-readable format for subsequent processing. Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle. The use of convolutional neural network (CNN) in recent developments has notably advanced HCR, leveraging the ability to extract discriminative features from extensive sets of raw data. Because of the absence of pre-existing datasets in the Kurdish language, we created a Kurdish handwritten dataset called (KurdSet). The dataset consists of Kurdish characters, digits, texts, and symbols. The dataset consists of 1560 participants and contains 45,240 characters. In this study, we chose characters only from our dataset. We utilized a Kurdish dataset for handwritten character recognition. The study also utilizes various models, including InceptionV3, Xception, DenseNet121, and a customCNNmodel. To show the performance of the KurdSet dataset, we compared it to Arabic handwritten character recognition dataset (AHCD). We applied the models to both datasets to show the performance of our dataset. Additionally, the performance of the models is evaluated using test accuracy, which measures the percentage of correctly classified characters in the evaluation phase. All models performed well in the training phase, DenseNet121 exhibited the highest accuracy among the models, achieving a high accuracy of 99.80% on the Kurdish dataset. And Xception model achieved 98.66% using the Arabic dataset. [ABSTRACT FROM AUTHOR]