Comparison of Two Different Feature Sets for Offline Recognition of Handwritten Arabic Words

Normalization is a very important step in automatic cursive handwritten word recognition. Based on an offline recognition system for Arabic handwritten words which uses a semi-continuous 1-dimensional HMM recognizer two different feature sets are presented. The dependencies of the feature sets from normalization steps is discussed and their performances are compared using the IFN/ENIT - database of handwritten Arabic words. As the lower and upper baseline of each word are part of the ground truth (GT) of the database, the dependency of the feature set from the accuracy of the estimated baseline is evaluated.