<p>Today, developments of AI (Artificial Intelligence), particularly in the domains of ML (Machine Learning) and DL (Deep Learning), offer novel chances to create tools that help the task of professionals in various fields. The field appears to be very far away from the clinical technology domains. An instance of these fields is olden EHW Egyptian hieroglyphics (EHW). Previously, the capabilities of various Convolutional Neural Networks (CNNs) were employed for EHW to the English language through the implementation of Image Processing (IP) and Natural Language Processing (NLP) methods integrated with AI methods. In the previous works, CNN is used to automatically translate olden hieroglyphic language by the digitized imageries to English text, but with a few restrictions, mostly associated with size and IQ (Image Quality), are also stated. This study suggests OGlyphnet Glyphnet optimized with Neophron Percnopterus Optimization (NPO) to resolve these problems. Glyphnet’s complex features contain the batch size, amount of epochs, kernel’s size and type, LR (Learning Rate), momentum, AF (Activation Function), dropout, and the convolution layer. However, the life cycle of Neophron percnopterus impacts optimization methods to address the complexity and progress the Glyphnet accuracy. Test classification experiments were executed to relate the classical CNN and the recently suggested one, hereafter mentioned as Glyphnet. The outcomes validate that the DL techniques perform very well by classification proportions, with Glyphnet overtaking the verified CNNs.</p>