INTERNATIONAL RESEARCH JOURNAL OF MODERNIZATION IN ENGINEERING TECHNOLOGY AND SCIENCE

Abstract

My project focuses on solving the problem of handwritten digit recognition, a key challenge in pattern classification. By using the MNIST dataset, which contains images of digits (0-9), the project compares the performance of different classification models, particularly Convolutional Neural Networks (CNN) and traditional Neural Networks. 1The aim is to demonstrate that CNN provides superior accuracy and computational efficiency in recognizing these digits. Tools like NumPy, Pandas, TensorFlow, and Keras are used for implementing the model. Results show that CNN significantly outperforms traditional Neural Networks in both speed and accuracy, making it an effective approach for handwritten digit recognition. The recognition of handwritten digits has long been an open challenge in the field of pattern recognition. Numerous studies have demonstrated that Neural Networks exhibit outstanding performance in data classification tasks. This paper aims to provide efficient and reliable techniques for the recognition of handwritten numerals by comparing various established classification models. Specifically, we focus on evaluating the performance of Convolutional Neural Networks (CNNs) in contrast with other classification approaches.3Handwritten digit recognition technology involves the automated identification of handwritten numbers by computers or related devices, with promising applications in postal code identification, financial statement processing, and bank bill management. This paper uses the MNIST handwritten digit database as a dataset to examine the performance of various algorithmsnamely, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Backpropagation (BP) Neural Network, and Convolutional Neural Network (CNN)in handwritten digit recognition. In the training process, we implement KNN in Python, use the scikit-learn library for SVM, and utilize TensorFlow for BP and CNN fine-tuning parameters to achieve optimal performance for each algorithm. Finally, by comparing the recognition rate and processing time of the four The primary challenge in handwritten numeral recognition stems handwriting varies in terms of size, orientation, stroke thickness, from the diversity in writing styles across individuals. Each persons Algorithms, we analyze their respective strengths and weaknesses in handwritten digit recognition. This revised version emphasizes key information and provides a clearer outline of your research approach.

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