Handwritten Arabic Numeral Recognition with MATLAB Implementation

In this documentation, I will demonstrate how to implement handwritten Arabic numeral recognition using MATLAB. First, we need to understand the background and significance of handwritten digit recognition. With the advent of the digital era, handwritten digit recognition has become increasingly important, with applications spanning various fields including automation, artificial intelligence, and machine learning. We will then explore MATLAB's application in handwritten digit recognition in detail. MATLAB is a powerful numerical computing and data analysis tool with comprehensive image processing and pattern recognition capabilities. Specifically, we will cover how to utilize MATLAB's Image Processing Toolbox for preprocessing steps such as image binarization, noise removal, and segmentation, along with the Machine Learning Toolbox for implementing classification algorithms like k-Nearest Neighbors (k-NN) or Support Vector Machines (SVM). Key functions such as imread() for image input, imbinarize() for thresholding, and fitcknn() for classifier training will be discussed. Finally, I will provide practical examples including code snippets demonstrating complete workflows from image preprocessing to classification accuracy evaluation, helping readers better understand and apply these techniques. Through this documentation, readers should gain fundamental knowledge of the principles and methods for implementing handwritten Arabic numeral recognition using MATLAB.