Abstract
In this paper, a novel strategy based on a hybrid algorithm of Elephant Herding Optimization Big Bang–Big Crunch is provided to address the English character recognition problem with interference from external noise. A model used to recognize handwritten Hindi (Devanagari) characters is called Hindi OCR (optical character recognition). Despite the fact that only a few nature-inspired algorithms are used for OCR, none are used for the characters seen in natural scenes due to the difficulties presented by the various fonts, lighting, orientation, and texture variations. Also, Hindi’s curved and complicated architecture makes it challenging to separate and analyze the text or character. Better feature databases are created using the proposed Hybrid EHO–B3C model to facilitate categorization. Two standard datasets of English characters, Char74k and ICDAR03, are used to assess the performance of the novel Hybrid EHO–B3C on natural scene characters. The proposed model is also evaluated on the Devanagari handwritten character dataset, which is a dataset consisting of characters from the Hindi language. Results show 83% results on Char74k and 86.4% on ICDAR, comparable to most available techniques. We have also examined our algorithm for face recognition by examining it on five different datasets, namely MUCT, Faces94, Extended Yale B, Georgia Tech, and Grimace, and attained high recognition results, even though the data sets differ in size, image size, brightness and ethnicity of people.
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Data availability
The Char74k data used in this study is publicly accessible and can be downloaded from the http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/ website. After defining the region of study, the datasets tab allows users to directly identify and access images.
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Goel, L., Kanhar, J., Patel, V.S. et al. Hybrid Elephant Herding Optimization–Big Bang Big Crunch for pattern recognition from natural images. Soft Comput 28, 3431–3447 (2024). https://doi.org/10.1007/s00500-023-08667-y
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DOI: https://doi.org/10.1007/s00500-023-08667-y