MATLAB Implementation for Handwritten Digit Recognition

Handwritten digit recognition is a technology that identifies handwritten numerical characters. The implementation process involves several key steps: First, extract the skeleton of the handwritten digit, which represents the main contour of the numerical character. This can be achieved using MATLAB's morphological operations like `bwmorph` with the 'thin' parameter to obtain a one-pixel-wide skeleton. Next, multiple straight lines are drawn at different angles to intersect with the digit skeleton. The intersection points and digit endpoints are counted and recorded as feature vectors, which describe the shape and structural characteristics of the handwritten digit. In MATLAB implementation, this involves using line equations and checking for intersections with the skeleton pixels. Finally, a feature database is established to store and match these feature vectors using pattern recognition techniques. The matching process typically employs distance metrics like Euclidean distance or more advanced classifiers such as k-nearest neighbors (KNN) implemented through MATLAB's Classification Learner app or custom functions. Through this systematic approach, we can achieve more accurate identification and matching of handwritten digits, thereby improving the reliability and accuracy of handwritten digit recognition systems.