[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122366-en":3,"doc-seo-122366-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122366,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Handwritten Digit Recognition using Machine Learning - A comparison of Gaussian Naive Bayes and Linear Discriminant Analysis","Handwritten Digit Recognition remains a benchmark in pattern recognition because diverse handwriting creates inherent classification difficulties. This study compares two classical statistical classifiers, Gaussian Naive Bayes and Linear Discriminant Analysis, for digit recognition on the MNIST dataset. Using 60,000 training and 10,000 test samples, performance is assessed with accuracy, precision, recall, F1 score, and confusion matrices. Results show GNB attains 55.58% accuracy, while LDA reaches 87.30%, indicating stronger discrimination of visually similar digits. The findings emphasize GNB’s independence limitation and LDA’s ability to capture shared variance across classes.","University of Central Florida  \nSTARS  \nData Science and Data Mining  \nMay 2025  \nHandwritten Digit Recognition using Machine Learning  \nDipok Deb  \nPhD Student, Big Data Analytics, UCF, [di876893@ucf.edu](di876893@ucf.edu)  \n Part of the Data Science Commons  \nFind similar works at: [https://stars.library.ucf.edu/data-science-mining](https://stars.library.ucf.edu/data-science-mining)  \nUniversity of Central Florida Libraries [http://library.ucf.edu](http://library.ucf.edu)  \nThis Article is brought to you for free and open access by STARS. It has been accepted for inclusion in Data Science and Data Mining by an authorized administrator of STARS. For more information, [please contact STARS@ucf.edu](please contact STARS@ucf.edu).  \nSTARS Citation  \nDeb, Dipok, \"Handwritten Digit Recognition using Machine Learning\" (2025) . Data Science and Data Mining. 40.  \n[https://stars.library.ucf.edu/data-science-mining/40](https://stars.library.ucf.edu/data-science-mining/40)  \nHandwritten Digit Recognition using Machine  \nLearning  \nDipok Deb  \nSTA 6366  \nStatistics and Data Science Department  \nUniversity of Central Florida  \nAbstract—Handwritten Digit Recognition (HDR) remains a fundamental benchmark in pattern recognition and machine learning due to its practical applications and inherent classifcation challenges posed by diverse handwriting styles. This study investigates and compares two classical statistical classifers—Gaussian Naive Bayes (GNB) and Linear Discriminant Analysis (LDA)—to recognize the digits from the MNIST dataset. Both models assume underlying normality in feature distributions and offer computational effciency, making them suitable for high-dimensional input such as image pixels. Using 60,000 training and 10,000 test samples, we evaluate model performance through accuracy, precision, recall, F1 score, and confusion matrices. The results reveal that while GNB achieves moderate accuracy (55.58%), LDA signifcantly outperforms it with an accuracy of 87.30%, demonstrating superior capability in distinguishing visually similar digits. Our analysis further highlights the limitations of GNB’s independence assumption and underscores LDA’s strength in capturing shared variance across classes. These fndings reinforce the effectiveness of LDA as a robust baseline for HDR tasks, especially when interpretability and computational simplicity are desired.  \nIndex Terms—Machine Learning, Handwritten Digit Recognition, MNIST Dataset, Multiclass Classifcation, Image Classifcation.  \nI. INTRODUCTION  \nHandwritten digit recognition (HDR) is a key problem in the domains of pattern recognition and machine learning [1] . It focuses on categorizing handwritten digits into one of ten classes (0-9) using pixel data from images. This task holds signifcant real-world value in applications such as postal mail sorting, automatic bank check processing, and digitizing historical records. Despite being conceptually straightforward, HDR presents several challenges due to the variability in handwriting styles, line thickness, orientation, and the presence of noise in the images.  \nOver time, the application of machine learning methods to handwritten digit recognition has advanced considerably. These methods seek to learn distinguishing features from image data and build accurate models for digit classifcation. From a statistical point of view, the goal of this project is to evaluate and analyze a machine learning classifer to accurately recognize handwritten digits from the MNIST dataset. Gaussian Naive Bayes (GNB) and Linear Discriminant Analysis (LDA) are two established, computationally effcient classifers based on statistical principles. The MNIST dataset contains grayscale pixel data, which can be treated as continuous  \nvariables. Gaussian Naive Bayes, a variant of Naive Bayes, assumes that the features follow a Gaussian distribution, making it a suitable option for continuous data like pixel intensities. Similarly, LDA assumes a normal distri","cbCaiieOJ4FIYtho","https://ap.wps.com/l/cbCaiieOJ4FIYtho","pdf",640947,1,7,"English","en",105,"# Introduction\n# Methodology\n## Dataset\n## Methods\n## Evaluation Metrics\n# Data Analysis\n## Dataset Exploration","[{\"question\":\"What task does the document address?\",\"answer\":\"It addresses handwritten digit recognition, classifying digits 0–9 from image pixel data using the MNIST dataset.\"},{\"question\":\"Which classifiers are compared and why?\",\"answer\":\"Gaussian Naive Bayes and Linear Discriminant Analysis are compared because they are classical, computationally efficient statistical classifiers with different distribution and independence assumptions.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is evaluated using accuracy, precision, recall, F1 score, and confusion matrices to identify misclassified examples.\"}]","Handwritten Digit Recognition using Machine Learning - A comparison of Gaussian Naive Bayes and Linear Discriminant Analysis | PDF",1785810268,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"handwritten-digit-recognition-using-machine-learning-a-comparison-of-gaussian-naive-bayes-and-linear-discriminant-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/handwritten-digit-recognition-using-machine-learning-a-comparison-of-gaussian-naive-bayes-and-linear-discriminant-analysis/122366/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What task does the document address?","Question",{"text":75,"@type":76},"It addresses handwritten digit recognition, classifying digits 0–9 from image pixel data using the MNIST dataset.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which classifiers are compared and why?",{"text":80,"@type":76},"Gaussian Naive Bayes and Linear Discriminant Analysis are compared because they are classical, computationally efficient statistical classifiers with different distribution and independence assumptions.",{"name":82,"@type":73,"acceptedAnswer":83},"How is model performance evaluated?",{"text":84,"@type":76},"Performance is evaluated using accuracy, precision, recall, F1 score, and confusion matrices to identify misclassified examples.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]