A Period Prediction System for Sinhala Epigraphical Scripts using Ensemble CNNs and Attention Modules

Identifying the period of epigraphical scripts is crucial for archaeologists and others to determine the age of inscriptions. Since different sets and shapes of letters were used in different eras, identifying the period of an epigraphical script also aids in recognizing these scripts. An ideal period prediction system should detect the era of an epigraphical script in real time with high accuracy. The objective of this study is to develop an automated system to predict the period of Sri Lankan Sinhala epigraphical scripts. In the first stage, a dataset of Sinhala epigraphical letter images was created using 7,012 samples from estampage pictures of Sri Lankan inscriptions, addressing the absence of a proper dataset. The proposed approach is more efficient than previous models as it can detect the period of individual letters as well as the period of raw, whole estampage images. Moreover, the approach incorporates a mechanism to detect the period of letters from inscriptions written between two consecutive eras. An ensemble CNN model with attention modules is utilized to identify the eras of epigraphical scripts. Experimental results show that the proposed system achieves an average classification accuracy of 93.88% in identifying the era of individual letters. The system can automatically determine the era of an inscription by analyzing its estampage image within thirty seconds.