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Hand-written character recognition and its conversion to text font from digital images automatically is one of the challenging tasks in the statistical pattern recognition and artificial intelligence. Improving the performance of the models depends on the input data, machine or deep learning model and on the appropriate selection of hyper-parameter optimization methods. This study investigates systematic exploration and optimization of hyper-parameters for traditional machine learning and deep learning models on a real time handwritten dataset.