A Handwritten Digit Recognition Algorithm using Two-Dimensional Hidden Markov Models for Feature Extraction
Jay Wierer, Nigel Boston · 2007
We propose a handwritten digit recognition algorithm that uses 4×4 2D hidden Markov models to extract basic features from an unclassified image. The novel idea given here is that we use powerful techniques from the emerging mathematical fields of tropical geometry and algebraic statistics to determine parameters for the model. The distance between the unclassified images and prototypes is calculated in stages, where estimates of the distance become finer as obviously distant prototypes are discarded from the pool of possible K-nearest neighbors. Our algorithm achieves a 95.51 percent recognition rate with zero rejection on the MNIST database of handwritten digits.