Comparison of Different Preprocessing and Feature Extraction Methods for Offline Recognition of Handwritten ArabicWords

Authors:
Haikal El Abed;Volker Margner
Affiliations:
Technical University Braunschweig, Germany;Technical University Braunschweig, Germany
Venue:
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Year:
2007

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Abstract

Preprocessing and feature extraction are very important steps in automatic cursive handwritten word recognition. Based on an offline recognition system for Arabic handwritten words which uses a semi-continuous 1-dimensional Hidden Markov Model recognizer, different preprocessing combined with different feature sets are presented. The dependencies of the feature sets from preprocessing steps are 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 of the database, the dependency of the feature set from the accuracy of the estimated baseline is evaluated.