Implementation of DTW Template Matching Algorithm for 0-9 Digit Recognition

This project implements digit recognition for numbers 0-9 using Dynamic Time Warping (DTW) template matching algorithm. The DTW algorithm performs similarity comparison between time-series data by measuring the optimal alignment between an input digit sequence and predefined template sequences. The core implementation typically involves calculating a cost matrix using Euclidean distance between data points, followed by dynamic programming to find the warping path with minimum cumulative distance. Key implementation steps include: 1. Template preprocessing: Normalizing and segmenting reference digit templates 2. Feature extraction: Converting input signals into comparable time-series vectors 3. DTW distance calculation: Implementing dynamic programming with recurrence relation dp[i][j] = cost[i][j] + min(dp[i-1][j], dp[i][j-1], dp[i-1][j-1]) 4. Classification: Selecting the template with minimum warping distance as recognition result The algorithm demonstrates particular effectiveness in handling temporal variations and different execution speeds, making it widely applicable in speech recognition, handwriting recognition, and gesture recognition systems. Its warping path optimization allows for robust pattern matching despite timing inconsistencies, ensuring high accuracy and stability in practical applications.