CNN-POWERED MARATHI SIGN DIALECT ACKNOWLEDGMENT AND TRANSLATION

This paper presents a comprehensive review of recent advancements in Marathi Sign Dialect (MSL) recognition and interpretation, focusing on the application of Convolutional Neural Networks (CNNs). Existing research demonstrates the effectiveness of CNNs in extracting and classifying features from MSL gestures, leading to promising accuracy rates. Despite these advancements, challenges such as data scarcity and the inherent complexity of MSL remain. To address these limitations, future research should explore hybrid approaches that integrate CNNs with Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) systems. By combining the strengths of these techniques, we can anticipate further improvements in MSL recognition and interpretation, ultimately enhancing communication accessibility for the Marathi sign language community