Deciphering Ancient Scripts Using Deep Learning-Based Sequence-To-Sequence Models

Deep learning-based sequence-to-sequence (S2S) models—such as Long Short-Term Memory (LSTM) networks—offer a sophisticated approach for translating ancient script characters to their corresponding English equivalents in the present. Nowadays, most of the solutions accessible are based on statistical models and rule-based language approaches. This is true because intricate grammatical systems and incomplete collections are involved. These limitations prevent perfectly correct interpretation and translation from scripts. This paper provides an S2S model that uses LSTM networks to translate from ancient scripts to modern languages at the character and word levels. This is in reaction to the previously described limitations. The model uses attention processes to identify relevant input sequences, enhancing the translation's accuracy.Using transfer learning enhances performance on ancient languages with less resources. This approach involves learning from like language data. Cuneiform and antique characters like hieroglyphs help to test the scheme. The findings show that the approach effectively recreates deleted text sections and enhances translating consistency. The trials reveal that the model is more stable and precise than the standard methods. This is the result of the model using a more exact and scalable decoding method for ancient scripts.