One of the most crucial tasks in the fields of computer vision and image processing is handwritten character recognition. Convolutional neural networks (CNNs), a subtechnology of artificial intelligence, have demonstrated encouraging advances in resolving this issue. The AlexNet model is one of the most well-liked and effective CNN architectures among the others. In this research, we use the CHAR74K dataset to examine the performance of the AlexNet model for handwritten character recognition. First, we establish the requirements for the application of handwritten character recognition. After that, we continue our examination of numerous original publications by eminent scientists who have taken on the challenge of handwritten character identification in multiple languages.. Next, we discuss our proposed approach to recognizing handwritten characters using the CHAR74K dataset. We then describe the architecture of the model and its training process, including the use of data augmentation and dropout regularization. Subsequently, we perform experimental analysis on the trained model and determine its accuracy. Our experimental outcomes show that the AlexNet model achieves a high accuracy of over 98% on the test set, outperforming previous state-of-the-art models. Our study demonstrates the effectiveness of the AlexNet model for handwritten character recognition and provides insights into the factors that affect its performance.