![]() Moreover, a channel attention module is included to boost the character-wise features in classification. ![]() Deep transfer learning techniques are utilized to train a CNN model with fewer numbers of samples. Since no previous studies were conducted, we have constructed a dataset for Sinhala ancient characters. In this paper, we propose a novel approach to classify the era of Sinhala epigraphical scripts into five different periods using the images of Sri Lankan inscriptions. Period prediction of epigraphical scripts is an important initial step in automated inscription character recognition systems, and also it helps archaeologists to find the era of an inscription in real-time. The study of recognizing epigraphical scripts is a challenging task since the shapes of the characters were changed over the time and different sets of characters were used in different eras. ![]() Inscriptions are important resources to know our history.
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