Offline Handwritten English Alphabet Recognition (OHEAR)
DOI:
https://doi.org/10.21928/uhdjst.v6n2y2022.pp29-38Keywords:
Alphabet Recognition, Handwriting Recognition, Multi-Class Support Vector Machine, Feature Extraction, Optical Character RecognitionAbstract
In most pattern recognition models, the accuracy of the recognition plays a major role in the efficiency of those models. The feature extraction phase aims to sum up most of the details and findings contained in those patterns to be informational and non-redundant in a way that is sufficient to fen to the used classifier of that model and facilitate the subsequent learning process. This work proposes a highly accurate offline handwritten English alphabet (OHEAR) model for recognizing through efficiently extracting the most informative features from constructed self-collected dataset through three main phases: Pre-processing, features extraction, and classification. The features extraction is the core phase of OHEAR based on combining both statistical and structural features of the certain alphabet sample image. In fact, four feature extraction portions, this work has utilized, are tracking adjoin pixels, chain of redundancy, scaled-occupancy-rate chain, and density feature. The feature set of 27 elements is constructed to be provided to the multi-class support vector machine (MSVM) for the process of classification. The OHEAR resultant revealed an accuracy recognition of 98.4%.
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Copyright (c) 2022 Hamsa D. Majeed, Goran Saman Nariman
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