Detection of Gujarati Handwritten Characters using Artificial Intelligence Techniques: Challenges, Opportunities, and Future Directions

Abstract

The handwritten character recognition (HCR) has grown significance in document digitisation, multilingual text processing, and automated form interpretation. Among the different Indic scripts it is relatively underexplored, despite a large population of speakers. This paper provides a comprehensive review of the state of the art of Artificial Intelligence (AI) models for the detection and recognition of characters from Gujarati handwritten text. We critically review over 30 work references focusing on the benefits and limitations of traditional machine learning (ML) methods, hybrid models and state-of-the-art deep learning (DL) architectures. We study the unique features of the Gujarati Script including diacritical marks and visually similar subset of characters and their impact on identification quotient. In addition, we discuss important challenges such as the lack of large-scale public datasets, variations in handwriting styles, and the details of Gujarati ligatures. By synthesising current knowledge, identifying methodological gaps, and providing potential future directions, this paper aims to assist both novices and experienced researchers in designing robust, efficient, and scalable solutions for Gujarati HCR.