Pigpen Cipher 9-Grid Characters Classification Using Enhanced CNN Deep Learning Architecture

In cryptography, breaking coded messages dates back many years. It often involves the use of various encoding techniques to obscure information. One such method is the Pigpen cipher, which substitutes letters with symbols from a grid layout. This study employs quad research grid characters with nine Pigpen ciphers to classify characters using EnhancedCNN deep learning techniques. For the most vulnerable in society, social safety nets are essential and decoding encrypted information can help improve the security of these systems from within. Pigpen character classification based on a structural model proposed by EnhancedCNN parallelizes training networks, which will help create an active environment among 27 classes of people in the modern field of social security. A total of 75,600 images was used in training and to validate the model, driving home the point that society needs to invest in advanced technology such as data analysis techniques responsible for better social outcomes. Finally, after five training sessions and an effective batch size of 27, iteration was completed. Continual refinement of protective systems for the vulnerable. Testing indicated that the proposed EnhancedCNN model performed particularly well, with an accuracy rate of 98%. Moreover, new technology in the context of social security frameworks is illustrated by our findings. This can then have a wide variety of potential applications, including automatic decryption of ancient pigpen characters on documents, cryptanalysis and data extraction journalism. All of these require government or social support within which technology innovation development plays a central role. The results from this study will improve pattern recognition methods involving deep learning, made possible with significant consequences for places where visualized data needs to be accurately classified safeguarding vulnerable communities and social protection systems.