In this paper, an offline holistic handwritten Arabic text recognition system based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) classifiers is proposed. The proposed system consists of three primary stages: preliminary processing, feature extraction using PCA, and classification using the polynomial, linear, and Gaussian SVM classifiers. In this proposed system, text skeleton is first extracted and the images of the text are normalized into uniform size for extraction of the global features of the Arabic words using PCA. Recognition performance of this proposed system was evaluated on version 2 of the IFN/ENIT database of handwritten Arabic text using the polynomial, linear, and Gaussian SVM classifiers. The classification results of the proposed system were compared with the results produced by a benchmark. TRS that is depending on the Discrete Cosine Transform (DCT) method using numerous normalization sizes of Arabic text images. The experimental testing results support the effectiveness of the proposed system in holistic recognition of the handwritten Arabic text.
Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.
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