Handwriting Normalization by Zone Estimation using HMM/ANNs

Autores UPV
Año
CONGRESO Handwriting Normalization by Zone Estimation using HMM/ANNs

Abstract

Offline handwritten text recognition requires several preprocessing stages. Many different preprocessing techniques have been proposed in the literature based either on geometrical heuristics or on statistical models. Unfortunately, these approaches usually fail when dealing with short sentences or isolated words. One statistical technique for text line preprocessing is based on the detection and classification of local extrema points, by means of neural networks, to determine the reference lines delimiting the different zones. This technique depends on a sufficient amount of local extrema and, relating its robustness, a single bad classified extrema point may lead to undesirable results. This paper proposes a novel method to normalize handwritten text lines based on a supervised statistical model which takes into account all pixels instead of just the local extrema. A Hidden Markov Model hybridized with an Artificial Neural Network is applied column-wise in order to segment each column of the handwritten line into three zones. The reference lines obtained in this way are used to normalize the image afterwards. The technique has been empirically tested on the IAM offline database.