Abstract
Optical character recognition systems enable human–machine interaction by using technology to discern published or handwritten letterings inside digital representations of physical documents; for instance, a scanned paper document, and these are widely utilized in a variety of applications. Other categorization techniques have been used to study Yoruba, English, Arabic, and other Latin characters. In light of this, it is worth noting that only a few articles have directly tackled the subject of Hausa character recognition using logistic regression (LR). The work develops a technique for the recognition of Hausa characters using LR. The system’s user interface was developed with C-sharp (C#) programming language. Data were gathered from many authors and scanned photographs were treated to some level of preprocessing to improve the quality of the digitized images, including text segmentation into individual characters, feature extraction, and recognition using LR. It was deduced that the recognition accuracy of the LR indicated 89% which outperformed other classifiers used on Hausa characters.
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Babatunde, A.N., Ogundokun, R.O., Jimoh, E.R., Misra, S., Singh, D. (2023). Hausa Character Recognition Using Logistic Regression. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_65
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DOI: https://doi.org/10.1007/978-981-99-0085-5_65
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