Abstract
Character recognition plays an important role in the modern world. In recent years, character recognition systems for different languages has gain importance. The recognition of Arabic writing is still an important challenge due to its cursive nature and great topological variability. The Artificial Immune System is a supervised learning technique that embodies the concepts of natural immunity to cope with complex classification problems. The objective of this research is to investigate the applicability of an Artificial Immune System in Offline Isolated Handwritten Arabic Characters. The developed system is composed of three main modules: preprocessing, feature extraction and recognition. The system was trained and tested with ten-fold cross-validation technique on an original realistic database that we built from the well-known IFN/ENIT benchmark. Parameter tuning was performed with a grid-search algorithm with leave-one-out cross-validation. The obtained results of the proposed system are promising with a classification rate of 93.25% and often outperform most well-known classifiers from Scikit Learn Library.