Hebrew letters Detection and Cuneiform tablets Classification by using the yolov8 computer vision model

TL;DR

This paper introduces a deep learning method using Yolov8 to detect Hebrew letters in cuneiform tablets, facilitating faster classification and deciphering of ancient scripts with minimal localized annotations.

Contribution

The study presents a novel approach leveraging transliterations and Yolov8 for Hebrew letter detection in cuneiform tablets, reducing the need for extensive sign localization annotations.

Findings

Effective Hebrew letter detection in cuneiform images

Improved sign detection accuracy through retraining

Enhanced tablet classification performance

Abstract

Cuneiform writing, an old art style, allows us to see into the past. Aside from Egyptian hieroglyphs, the cuneiform script is one of the oldest writing systems. Many historians place Hebrew's origins in antiquity. For example, we used the same approach to decipher the cuneiform languages; after learning how to decipher one old language, we would visit an archaeologist to learn how to decipher any other ancient language. We propose a deep-learning-based sign detector method to speed up this procedure to identify and group cuneiform tablet images according to Hebrew letter content. The Hebrew alphabet is notoriously difficult and costly to gather the training data needed for deep learning, which entails enclosing Hebrew characters in boxes. We solve this problem using pre-existing transliterations and a sign-by-sign representation of the tablet's content in Latin characters. We recommend one of the supervised approaches because these do not include sign localization: We Find the transliteration signs in the tablet photographs by comparing them to their corresponding transliterations. Then, retrain the sign detector using these localized signs instead of utilizing annotations. Afterward, a more effective sign detector enhances the alignment quality. Consequently, this research aims to use the Yolov8 object identification pretraining model to identify Hebrew characters and categorize the cuneiform tablets.