Deep Learning Based Approach for Historical Manuscript Dating

摘要

Digitization of historical manuscripts from premodern eras, has captivated the document analysis and pattern recognition community in recent years. Estimation of the period of production of such documents is a challenging yet favored research problem. In this paper, we present a deep learning based approach to effectively characterize the year of production of sample documents from the Medieval Paleographical Scale (MPS) dataset. By employing transfer learning on a number of popular pre-trained Convolutional Neural Network (CNN) models, we have significantly reduced the Mean Absolute Error (MAE) reported in previous studies.