Treffer: A new method for handwritten digit recognition compared with (LSTM) over with (SVM) algorithm.

The purpose of this study is to offer a unique approach for recognizing handwriting digits while comparing its effectiveness using two recognized computations, Long Short-Term Memory (LSTM) networks & Support Vector Machine (SVM) methodology. The purpose is to gauge the value of the recommended approach in obtaining outstanding precision and effectiveness in digit recognition exercises. Employed the data set MNIST, comprising 60,000 training photos & 10,000 testing scans of handwritten digits. Constructed a unique convolutional neural network (CNN) design suited for identification of digits Training Approach: The system underwent training for twenty trials using a stochastic gradient descent (SGD) optimized with a rate of training of 0.01 as well as a batch number of 128. Its structure consisted of a neural network (LSTM) that included two layers composed of LSTM next to a layer of high density with a soft max stimulation, as well as the Adam optimizer with a discovering rate of 0.001 was used. The LSTM structure was educated for 10 epochs, as well as the technique known as SVM with a radial basis function (RBF) a kernel was used for sorting. Hyperparameters like C (Measured the accuracy of each algorithm on the test dataset Recorded training time and inference time for each algorithm. Assessed robustness to noise and variations in handwriting styles. Conducted experiments on a machine with an Intel Core i7 processor and 16GB RAM. Implemented algorithms using Python with TensorFlow and scikit-learn libraries. Employed 5-fold cross-validation for robust evaluation Compare the accuracy achieved by the proposed method, LSTM, and PCA algorithms on the test dataset Discuss the computational resources required by each method, such as training time and inference speed Evaluate the robustness of each method against noise, distortion, and variations in handwriting styles. Summarize the key findings of the study, highlighting any notable differences in accuracy or computational efficiency between the methods. Discuss the implications of the results for practical applications of handwritten digit recognition, such as in digitized document processing or automatic form filling. Suggest potential avenues for future research, including the exploration of hybrid approaches that combine the strengths of different methods or the adaptation of the novel method for other classification tasks. Acknowledge any limitations of the study, such as dataset bias, model overfitting, or constraints imposed by computational resources. Emphasize the significance of the study in advancing the state-of-the-art in handwritten digit recognition and its potential impact on related fields. [ABSTRACT FROM AUTHOR]