Augmentation of base classifier performance via HMMs on a handwritten character data set
This work addresses recognition accuracy for handwritten Latin alphabet characters, but it is incremental as it applies existing HMM methods to a specific dataset.
The paper tackled handwritten character recognition by enhancing base classifiers with Hidden Markov Models (HMMs) for error correction, achieving a best performance of 89.8% accuracy.
This paper presents results of a study of the performance of several base classifiers for recognition of handwritten characters of the modern Latin alphabet. Base classification performance is further enhanced by utilizing Viterbi error correction by determining the Viterbi sequence. Hidden Markov Models (HMMs) models exploit relationships between letters within a word to determine the most likely sequence of characters. Four base classifiers are studied along with eight feature sets extracted from the handwritten dataset. The best classification performance after correction was 89.8%, and the average was 68.1%