An AI Based Automatic Translator for Ancient Hieroglyphic Language—From Scanned Images to English Text

Abstract

Recent advancements in the fields of Machine Learning and Deep Learning made a huge transformation in other fields that are not related to Computer Science. In this work, a new framework is proposed to tackle the problem of translating the old Egyptian Hieroglyphic writings to English language through deploying both Image Processing and Natural Language Processing techniques combined with AI approaches. Our primary goal is to design an application that completely revolutionizes a tourist's experience while navigating Egyptian Historical sites. This work utilize different AI techniques to automatically convert the scanned photos of hieroglyphic language to understandable and readable English language, through two main sub-tasks: The automatic detection and recognizing of the scanned glyphs images and the translation of them into English language. Different data sources of this low-resource language were explored and augmented to train and test our models. Results of different models and algorithms are assessed and analyzed to evaluate our work. State-of-the-art results are achieved compared to literature in both automatic glyphs recognition, and glyphs-to-English translation.

Analyzing the impact of deep learning algorithms and fuzzy logic approach for remote English translation

“…Fuzzy algorithms' primary strength lies in their feature extraction capabilities. The collected variables and attributes can be used to generate data that is acceptable for English translation 9 . The English translation systems are another domain were fuzzy logic-based paradigm is employed.…”