Ensemble machine learning model for classification of handwritten digit recognition

Recognizing handwritten numbers is a commonly used application in machine learning. It is used in various applications, including postal mail sorting zip code recognition, writer identification and verification, diacritical processing, and recognition of handwritten digits on a bank check. Each of the 10 digits (0-9) is designated as a category in the context of machine learning-based classification tasks. However, due to the variety of handwriting styles from one person to another, it difficult for a person to distinguish. In addition, each learning algorithm may have its own set of benefits and drawbacks, implying that one algorithm may be able to learn some but not all of the special aspects of handwritten numbers. In order to enhance the effectiveness of the categorization process, a technique for reading handwritten numbers has been presented. Using a convolutional neural network architecture, the image properties of handwritten numerals are retrieved (CNN). Additionally, to train the classifiers, Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression Model were utilized (LRM). The group classifier was able to achieve a recognition accuracy of 98 percent in the experiments using the MNISET dataset.