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Advancing deep learning techniques for low-resource Shahmukhi Punjabi language processing

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  • Muhammad Shabbir
  • Mudassir Iftikhar

Abstract

Shahmukhi Punjabi, over 18 million of Pakistani speaks the Shahmukhi Punjabi language but there is not proper research is being available yet. So this paper explores the software of named entity recognition (NER), recurrent neural network (RNN), and long short-term memory (LSTM) models on a dedicated dataset. The study includes a thorough analysis of loss graphs, accuracy measures, and understanding matrices. Our contribution is looks into Shahmukhi Punjabi's underappreciated work on a variety of sophisticated complex space models, including NER and network RNN. The majority of current research focuses on languages that are widely spoken. By implementing LSTM, RNN, and NER models and evaluating their efficiency on specific Shahmukhi Punjabi data, this work seeks to close this gap on Shahmukhi language research. Our LSTM models give us the 82% accuracy and RNN give us the 82.57%.

Suggested Citation

  • Muhammad Shabbir & Mudassir Iftikhar, 2026. "Advancing deep learning techniques for low-resource Shahmukhi Punjabi language processing," International Journal of Complexity in Applied Science and Technology, Inderscience Enterprises Ltd, vol. 2(3), pages 228-245.
    • Handle: RePEc:ids:ijcast:v:2:y:2026:i:3:p:228-245
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