Viterbi Recognition Code with HMM Models for Digits 0-9

This implementation provides Viterbi recognition code along with Hidden Markov Models (HMM) specifically trained for digits 0-9. The code is developed using MATLAB programming language and has undergone extensive testing and debugging, ensuring successful deployment in practical applications. The core algorithm employs the efficient Viterbi algorithm for sequence labeling and recognition. The implementation calculates and compares joint probabilities between observation sequences and HMM models to identify the most probable hidden state sequence. Key algorithmic components include dynamic programming for path probability computation and backtracking for optimal path recovery. For digit recognition, we constructed specialized HMM models for digits 0-9 through careful parameter estimation and training. Each model captures the unique temporal patterns and feature transitions characteristic of its respective digit, enabling accurate identification and prediction within input sequences. The MATLAB implementation leverages the language's simplicity, learning efficiency, and flexibility. The code structure allows straightforward extension and modification, with clear function separation between model loading, probability calculation, and path decoding modules. Rigorous testing and debugging procedures have validated the code's correctness and stability. The implementation demonstrates robust performance in real-world scenarios, delivering accurate and reliable digit recognition capabilities suitable for production environments.