{'fraction_non_alphanumeric': 0.017745302713987474, 'fraction_numerical': 0.0041753653444676405, 'mean_word_length': 5.187096774193549, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Handwritten Digit Recognition System based on CNN and SVM

The recognition of handwritten digits has aroused the interest of the scientific community and is the subject of a large number of research works thanks to its various applications. The objective of this paper is to develop a system capable of recognizing handwritten digits using a convolutional neural network (CNN) combined with machine learning approaches to ensure diversity in automatic classification tools. In this work, we propose a classification method based on deep learning, in particular the convolutional neural network for feature extraction, it is a powerful tool that has had great success in image classification, followed by the support vector machine (SVM) for higher performance. We used the dataset (MNIST), and the results obtained showed that the combination of CNN with SVM improves the performance of the model as well as the classification accuracy with a rate of 99.12%.