Handwritten digit recognition technology is a process of identifying digits automatically using a computer or another platform. It holds significant promise applications in Optical Character Recognition (OCR), biometric and signature verification, and suspect identification. This study utilizes the widely recognized MNIST handwritten digital database as the dataset and deliberates algorithms: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Convolutional Neural Networks (CNN), and deep learning for digital recognition. The research employs KNN, SVM, BPNN, and CNN with TensorFlow. The algorithm’s parameters are fine-tuned to attain optimal results for each method. This study aims to assess algorithm performance, comprehend their capabilities, and gain insights into real-world applications. Lastly, an analysis is conducted by evaluating the accuracy of recognition and duration of four algorithms, providing insights into the strengths and weaknesses of each method in the context of handwriting recognition.