Language and Era Prediction of Digitized Indian Manuscripts Using Convolutional Neural Networks

With an increasing number of Indian manuscripts being digitized, the subject of their era prediction is readdressed to interpret the socio-economic fabric of different periods. This paper describes a novel approach to estimate the era of Indian manuscripts from their scanned images using convolutional neural networks (CNN). The method primarily uses image processing to harness visual features from small image patches and classifies them based on the difference in writing styles in terms of strokes and letter formation. We follow a two-step approach of language prediction followed by a separate era prediction model for each language to achieve optimal results. For this paper, we restrict consideration to six Indian language manuscripts written between the sixteenth and twentieth centuries. Conclusively, our model outperforms other well-known architectures and gave over 90% and 80% accuracy on the training and validation data, respectively.