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    Using optical character recognition techniques, classification of documents extracted from images
    (Springer Science and Business Media Deutschland GmbH, 2023) Ibrahim, Abdullahi Abdu; Safaa Salim, Ahmed; Ameer Abd Almaged, Husam
    We now have autonomous cars, speech recognition, efficient online search, and a far greater grasp of the human genome thanks to machine learning techniques developed during the previous ten years. Today, machine learning is employed so often that many times are mistakenly made. It is possible to educate a computer to anticipate outcomes that are challenging for the human brain by trying to teach it certain processes or scenarios. Additionally, these techniques enable us to quickly do various tasks that are frequently impractical or challenging for humans to complete. These factors make machine learning so crucial in today's world. There are two distinct machine learning techniques that were employed in this study. The manuscript materials were moved to the computer and then categorized to address a real-world issue. To complete the procedure, we relied on three fundamental techniques. A scanner or digital camera has converted handwriting or printed materials to digital format. Two alternative optical character recognition (OCR) operations have been used to process these papers. Next, the Naive Bayes method is used to categorize the sentences that were created. The entire project was created using the Windows operating system and Microsoft Visual Studio 12. All components of the study were written in the C# programming language. DLLs and prepared codes were also employed. Exploited.