Improved Deep Learning model for Ancient Cuneiform Symbols Classification

Raed Majeed^1,*, Hiyam Hatem¹, Wael Abd-Alaziz¹

¹Collage of Computer Science and Information Techonology,University of Sumer, Iraq

Emails: raed.m.muttasher@gmail.com; hiamhatim2005@gmail.com; w.abdalaziz@uos.edu.iq

Abstract

Cuneiform script, among the earliest writing systems, poses a distinct challenge for classification because of its complex symbols and varied linguistic contexts. This study investigates the use of Convolutional Neural Network (CNN) architectures for the classification of cuneiform symbols. The preprocessing includes resizing the cuneiform images to a uniform dimension and categorizing them into training, validation, and testing sets. A modified CNN model has been introduced. The CNN model demonstrates a lower parameter count in comparison to other deep learning models, which frequently necessitate significant storage capacity. The results from the CLI dataset indicate that the proposed CNN model reached an impressive accuracy of 99.55%, This study enhances computational approaches for the analysis of ancient scripts and underscores the significance of utilizing deep learning techniques within the fields of historical linguistics and digital humanities.

Keywords: Cuneiform classification; Convolutional Neural Networks (CNNs); Symbol recognition; Ancient script analysis; Deep learning linguistics