@article{4652,
  author = {Pit Pichappan},
  title = {A Review of Viterbi Algorithm Applications in Pattern Recognition and Language Processing},
  journal = {International Journal of Computational Linguistics Research},
  year = {2026},
  volume = {17},
  number = {1},
  doi = {https://doi.org/10.6025/ijclr/2026/17/1/1-17},
  url = {https://www.dline.info/jcl/fulltext/v17n1/jclv17n1_1.pdf},
  abstract = {This work presents a comprehensive review of the Viterbi algorithm's applications in pattern recognition
and natural language processing (NLP). Originally developed for decoding convolutional codes, the Viterbi
algorithm has become a cornerstone technique for maximum a posteriori (MAP) sequence estimation in
Hidden Markov Models (HMMs). The paper outlines its theoretical foundation as a dynamic programming
method that efficiently identifies the most probable sequence of hidden states given observed data. It then
explores numerous domain specific adaptations such as modified trellis structures, context aware decoding,
beam search pruning, and finite state machine optimisations that enhance performance across diverse tasks.
These applications span distorted pattern recognition, named entity recognition (NER), stochastic grammar
parsing, sign and gesture recognition, contextual text recognition, language testing, letter to phoneme conversion,
and multilingual translation. In each domain, Viterbi based approaches consistently outperform
traditional baselines by improving accuracy, reducing computational complexity (often from exponential
or cubic to linear or quadratic time), and increasing robustness to noise and temporal distortions. The
review also highlights recent innovations integrating Viterbi decoding with deep learning, parallel computing
(e.g., GPU acceleration), and multimodal inputs.
A unified conceptual framework illustrates how multimodal inputs are processed through feature extraction
and probabilistic modelling before Viterbi based decoding yields optimised decisions. Despite its maturity,
the algorithm remains highly relevant due to its optimality, interpretability, and efficiency. However,
challenges persist including scalability in large state spaces, integration with modern neural architectures,
and the need for explainable, user centric decoding. The paper concludes that the Viterbi algorithm is not
obsolete but evolving, with ongoing research poised to further extend its utility in intelligent, real time
systems.},
}