Abstract
<p>Handwriting character recognition involves a high degree of variability and imprecision. For that, the main factor to judge the recognition accuracy is the technique that is used to extract the features. This paper developed a novel method for handwritten Arabic characters by combining the Density-Based Clustering method with statistical and morphological features. The first stage in recognition of handwritten character image has been done by binarization the image then applies noise removal techniques. The Density-Based Algorithm used to categorize and find any shape of clusters based on pixel information positions. This technique divided the image into characters. Each character will be decomposing into four regions from the centroid followed by feature extraction. These features include vertical and horizontal projections, upper and lower profile, rectangularity and orientation. The results of the present process will transfer to the Neural Network (NN) stage which generates a high level of correctness and accuracy by training. The testing results compared with two of state-of-art researches. The total accuracy of this proposed work observes a better recognition of characters.</p>
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