Knowledge Base-Based Handwritten Digit Recognition

This project involves developing a handwritten digit recognition system using MATLAB that leverages a knowledge base for enhanced functionality. The system utilizes image processing techniques and machine learning algorithms to accurately identify handwritten digits while querying a knowledge repository to provide supplementary information. Users can input images of handwritten digits, which the system processes through several key stages: image preprocessing (including noise reduction, binarization, and normalization), feature extraction using methods like HOG (Histogram of Oriented Gradients) or zoning algorithms, and classification through machine learning models such as SVM (Support Vector Machine) or neural networks. The recognized digit is then cross-referenced with the knowledge base, which may contain additional attributes, statistical data, or contextual information about the digit. This comparative analysis enables the system to offer related insights and suggestions, such as common handwriting variations or mathematical properties. Through this integrated approach, users gain deeper understanding of handwritten digit characteristics and enjoy a more comprehensive digit recognition experience that combines pattern matching with contextual intelligence.