Abstract
Arabic handwritten letter recognition (AHLR) is a challenging field in pattern recognition due to the different font styles, size, and noises. The font style is changed according to several criteria such as human skills, age, and culture. So, till now there is no such a system with sufficient accuracy. In the current paper, a new approach is introduced based on Moth-flame optimization (MFO) for AHLR, called (MFO-AHLR). The main goal of the proposed approach is to improve the accuracy of AHLR with a least number of features. MFO-AHLR consists of the following phases: pre-processing (including binarization and noise removal), feature extraction (including several kinds of features) and classification (which are kNN, RF, and LDA in this work). Between feature selection and classification, we adopt MFO as a feature selector. A benchmark dataset for Arabic handwritten letter images (CENPARMI) was used. The achieved results showed superior results for the selected features in all experiments. Also, for all classifiers, the selected feature sets outperformed the non-selected features; and the processing time was improved. The MFO-AHLR achieved 99.25% of classification accuracy which is the highest among the other published works. To the best of our knowledge, there is no AHLR achieved it.