This study investigates how different feature selection techniques enhance offline Gurmukhi character recognition. By analyzing various feature extraction methods and comparing their performance across machine learning models, the research identifies approaches that improve accuracy while reducing computational load. The findings support the development of more efficient and reliable OCR systems for Gurmukhi script, enabling better digital processing of handwritten and printed text. This work contributes to advancing regional language computing and intelligent text recognition technologies.
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National Academy Science Letters (SCI)
2024
https://link.springer.com/article/10.1007/s40009-024-01532-y