Issue No. 04 - April (2011 vol. 33)
Salvador España-Boquera , Universitat Politècnica de València, Valencia
Maria Jose Castro-Bleda , Universitat Politècnica de València, Valencia
Jorge Gorbe-Moya , Universitat Politècnica de València, Valencia
Francisco Zamora-Martinez , Universidad CEU-Cardenal Herrera, Alfara del Patriarca and Universitat Politècnica de València, Valencia
This paper proposes the use of hybrid Hidden Markov Model (HMM)/Artificial Neural Network (ANN) models for recognizing unconstrained offline handwritten texts. The structural part of the optical models has been modeled with Markov chains, and a Multilayer Perceptron is used to estimate the emission probabilities. This paper also presents new techniques to remove slope and slant from handwritten text and to normalize the size of text images with supervised learning methods. Slope correction and size normalization are achieved by classifying local extrema of text contours with Multilayer Perceptrons. Slant is also removed in a nonuniform way by using Artificial Neural Networks. Experiments have been conducted on offline handwritten text lines from the IAM database, and the recognition rates achieved, in comparison to the ones reported in the literature, are among the best for the same task.
Handwriting recognition, offline handwriting, hybrid HMM/ANN, HMM, neural networks, multilayer perceptron, image normalization.
S. España-Boquera, J. Gorbe-Moya, F. Zamora-Martinez and M. J. Castro-Bleda, "Improving Offline Handwritten Text Recognition with Hybrid HMM/ANN Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 767-779, 2010.