Browsing by Author "Zerrouki, Nabil"

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    DotWise: A deep Learning-Powered Application for Efficient Black-To-Braille Text Conversion
    (Institute of Electrical and Electronics Engineers Inc., 2025) Zemouri, ET-Tahir; Zerrouki, Nabil; Harrou, Fouzi; Sun, Ying; Sayah, Djamel Eddine
    Access to information and reading materials is crucial for the empowerment and education of visually impaired individuals, and Braille serves as a vital medium for this purpose. However, the production of Braille texts is often time-consuming and expensive, limiting the availability of resources. This paper presents an innovative automatic Braille recognition and conversion system, designed to significantly reduce both the time and cost involved in producing Braille texts. By automating the conversion of standard printed text into electronic Braille, the system addresses the challenges faced in Braille production, enhancing accessibility for the visually impaired. The core of the proposed system leverages deep learning techniques, specifically the Long Short-Term Memory (LSTM) algorithm, to recognize printed characters and convert them into Braille. Experimental results demonstrate a drastic reduction in conversion time, highlighting the efficiency of the approach. This system promises to expand the availability of reading materials for visually impaired individuals, making a meaningful impact on their knowledge acquisition and overall welfare