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Many government institutions in developing countries such as the Kurdistan Region of Iraq (KRI) keep a variety of paper-based records that are available in printed or handwritten format. The need for technology that turns handwritten writing into digital text is therefore highly demanded. E-government in developed and developing countries is a crucial facilitator for the provision of such services. This paper aims to develop a deep learning model based on the mask region convolutional neural network (mask-RCNN) to effectively digitize kurdish handwritten text recognition (KHTR). In this research, typical datasets, which includes the isolated handwritten Central Kurdish character images, an extensive database of 40,410 images, and 390 native writers have been produced to determine the developed approach's performance in terms of identification rates. This approach achieves adequate outcomes in terms of training time and accuracy. The proposed model gives higher performance for detection, localization, and recognition when using a dataset containing many challenges, the results were 80%, 96%, and 87.6 for precision, recall, and F-score respectively. The findings revealed that the proposed model obtained better results compared to other similar works. The accuracy of optical character recognition (OCR) is more than 99%. [ABSTRACT FROM AUTHOR]

Ocular disease is a term used to describe a wide range of illnesses that affect the eyes and visual system. These diseases can affect one or both eyes and can range from mild to severe. The use of machine learning algorithms to categorize ocular diseases has become an area of interest in the ophthalmology community. This study is to compare the performance of different machine learning algorithms in classifying ocular diseases based on fundus images. The dataset of fundus images of patients diagnosed with different ocular diseases like Cataracts, pathological myopia, glaucoma, age-related macular degeneration, and abnormalities are considered. Ocular Disease Intelligent Recognition (ODIR) has been used. The SeequzeNet and GoogleNet deep learning models with different machine learning algorithms employed in experimental work includes KNN, random forest, support vector machines, logistic regression, and gradient boosting. The performance of each algorithm is evaluated using accuracy, sensitivity, and specificity metrics. The results show that logistic regression outperforms the other algorithms in terms of accuracy, sensitivity, and specificity. The findings of this study suggest that machine learning algorithms, particularly Logistic Regression, can be useful in accurately classifying ocular diseases based on fundus images. Feature extraction using SeequzeNet achieved an accuracy of 71.6%, outperforming GoogleNet's accuracy of 68.2%. [ABSTRACT FROM AUTHOR]