Soft computing approaches for character credential and word prophecy analysis with stone encryptions.

The uniform corpus of untranslated script is a preliminary stage for computational epigraphy. Mechanizing this process through deep learning algorithms will be an essential support to the epigraphical research. Our proposed system based on soft computing techniques focuses on the progression of recognizing the eleventh-century ancient Tamil character and converting them into current-century word form. Initially, the system is implemented by performing preprocessing steps followed by image segmentation. The decomposed image undergoes a hybrid feature extraction technique along with Chi-square test to check whether entire pixel in image of Zernike is bounded inside the unit circle or not, whereas ANOVA method is used for testing the significant difference between HOG feature and zoning feature. These functions are subjected to image classification and proceeded with character recognition using convolutional neural networks. Finally, the identified character is progressed into word form with the help of boggle algorithm. The hybrid feature extraction along with convolutional neural networks is achieved with 92.78% of recognition rate accurately. Our experiment shows a large perspective of deep learning algorithms in automatic epigraphy. [ABSTRACT FROM AUTHOR]