A Novel Feature Extraction Method Based on Histogram and Mathematical Morphology for Isolated Handwritten Greek Characters Recognition

Abstract

The isolated handwritten character recognition with multiple styles is a challenging research problem. In this paper, we propose a novel method of features extraction for character recognition based on the mathematical morphology and histogram techniques into vertical, horizontal, diagonal and anti-diagonal directions, knowing that the features extarction method is an important step in many image processing tasks. In this context, we present two comparisons in isolated handwritten Greek characters recognition, in fact the first comparison is between the hybrid methods exploited in features extraction which are the mathematical morphology combined with the histogram method; in contrast the second comparison is performed in order to deduce what is the most powerful between third genres of distances used in classification The Euclidean, Manhattan, and Minkowski distances. For this purpose, we have pre-processing each character image with different techniques. Furthermore, in the experiments results we provide extensive comparisons which demonstrate that our method outperforms for different characters' recognition, the results that we have obtained demonstrates really in one hand the performance of a novel method used in features extraction and the Euclidean distance in classification in the other hand.

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“…AI models typically require a large amount of training data to effectively learn intricate features within a dataset. For example, the MNIST dataset used for image recognition consists of labeled data with 60,000 samples for training and 10,000 samples for evaluation ( El Kessab et al, 2013 ). Given the time-consuming nature of dataset creation, in particular labeling, utilizing prelabeled open data is a time-effective choice for developing AI.…”