AI-Based Sindhi Handwritten Alphabets Classification with Web-Based Development

Mudasir Murtaza, Farhad Ali, Muhammad Taha
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Mudasir Murtaza, Farhad Ali, Muhammad Taha: Department of Computer ScienceQuaid-e-Awam University of Engineering, Science and Technology Nawabshah, Pakistan

International Journal of Innovations in Science & Technology, 2025, vol. 7, issue 6, 187-195

Abstract: Handwriting recognition has made remarkable progress for some prominent scripts, but low-resource languages such as Sindhi have received little attention so far. In this research, we propose the design and implementation of a strong AI based model to classify handwritten Sindhi alphabets. To overcome the difficulties caused by varying handwriting and a lack of publicly available datasets, the model builds on a manually curated, heterogeneous dataset, sophisticated CNN architectures, and data augmentation techniques. To support more research, the dataset will be made publicly available in two versions: raw and augmented. This study’s key contributions include achieving approximately 93% training accuracy and 96% validation accuracy with a loss below 1%, and the creation of valuable open-source datasets for Sindhi handwriting recognition. While a web-based application is planned as future work, these achievements provide a strong foundation for digitizing Sindhi texts and educational tools, and help preserving Sindhi language heritage.

Keywords: Sindhi; Handwriting Recognition; Convolution Neural Network; Data Augmentation; Web-Based Application; Machine Learning; Open-source (search for similar items in EconPapers)
Date: 2025
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