A 2D Dynamic Programming Approach for Markov Random Field-based Handwritten Character Recognition

Abstract

This paper presents the use of a new 2D dynamic programming approach for handwritten character recognition. The theoret- ical natural extension of the well-known 1D dynamic programming algorithm has been presented recently within an hidden Markov random field modeling framework. This principle has been adapted to a handwritten character recognition task and the performances are analyzed on the MNIST database for which spectral local features are extracted. Preliminary results exhibit an error rate similar to the ones reported in the literature.