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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]

Recognition of handwritten digits has garnered significant interest among researchers in the domain of recognizing pattern. This interest stems from the recognition's relevance in various real-life applications, including reading financial checks and official documents, which has remained a persistent obstacle. To address this challenge, researchers have developed numerous algorithms focusing on recognizing handwritten digits across different human languages. This paper presents a new Kurdish Handwritten dataset, consisting of Kurdish characters, digits, texts, and symbols. The dataset consists of 1560 participants, encompassing a broad and varied group. It serves as the primary dataset for training and evaluating algorithms in Kurdish digit recognition. We used Kurdish dataset named (KurdSet) and Arabic dataset for handwritten recognition, which holds 70,000 images of Arabic digits that were written by 700 various participants. Additionally, various models are utilized in the study, including ResNet50, DenseNet121, MobileNet, and a custom CNN (convolutional neural network). Additionally, the models' effectiveness was assessed through the examination of test accuracy, which measures the percentage of correctly classified digits in the evaluation phase. ResNet50 also performs exceptionally well that achieved test accuracy 99.67%, indicating its All models exhibit good performance, DenseNet121 and the Custom CNN Model demonstrate the highest test accuracy of 99.73%, highlighting their superior performance. capabilities in capturing relevant features. Despite its accuracy, MobileNet still exhibits good recognition capability with a test accuracy 99.54%. [ABSTRACT FROM AUTHOR]