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A Novel Artificial Intelligence Approach to Optical Character Recognition of Conjunct Gujarati Script

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  • Dhananjay Patel
  • Himanshu Maniar
  • Jagin Patel

Abstract

This paper surveys recent advances in optical char- acter recognition (OCR) for the Gujarati script, with a focus on complex conjunct characters. Gujarati is an Indo-Aryan script spoken by ∼62 million people [1], [2], yet its OCR remains challenging due to intricate glyph shapes and extensive consonant clusters [3], [4]. We review how machine learning (ML), deep learning (DL), and NLP techniques have been applied to segment and recognize Gujarati text, especially conjunct lig- atures. Notable studies from 2012–2025 are examined, including ANN and CNN-based classifiers that achieve high accuracy on isolated conjuncts [3], [5]. Finally, ongoing challenges (data scarcity, variability of handwriting and fonts) and outline future directions such as transformer models and language-model integration for Gujarati OCR.

Suggested Citation

  • Dhananjay Patel & Himanshu Maniar & Jagin Patel, 2025. "A Novel Artificial Intelligence Approach to Optical Character Recognition of Conjunct Gujarati Script," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 12(5), pages 35-41, October.
    • Handle: RePEc:etm:ijsrst:v12:y2025:i5:id:1141
    DOI: 10.32628/IJSRST2513114
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