A new large Arabic database for offline handwriting recognition

Abstract

To evaluate the performances of handwriting recognition systems, it is necessary to compare them objectively on the same database. A few freely databases are available for Arabic handwriting recognition, for this reason we have developed a new database of Algerian village names to be available freely for research and academic use. Up to now the database contains 1,209 forms including 26,580 binary and greyscale images representing 886 Algerian village names, collected from 1,209 writers. We also describe a new character segmentation algorithm for offline handwritten Arabic words. This algorithm uses a set of fuzzy ART neural networks as classifiers and Zernike invariant moments as features. The proposed system was trained and tested for the first time using the new database. A height segmentation and recognition accuracy were reported.