Performance Analysis of Handwritten Text Augmentation on Style-Based Dating of Historical Documents

One of the main questions paleographers aim to answer while studying historical manuscripts is when they were produced. Automatized methods provide tools that can aid in a more accurate and objective date estimation. Many of these methods are based on the hypothesis that handwriting styles change over periods. However, the sparse availability of digitized historical manuscripts poses a challenge in obtaining robust systems. The presented research extends previous research that explored the effects of data augmentation by elastic morphing on the dating of historical manuscripts. Linear support vector machines were trained on k-fold cross-validation on textural and grapheme-based features extracted from the Medieval Paleographical Scale, early Aramaic manuscripts, the Dead Sea Scrolls, and volumes of the French Royal Chancery collection. Results indicate training models with augmented data can improve the performance of historical manuscript dating by 1–3% in cumulative scores, but also diminish it. Data augmentation using elastic morphing can both improve and decrease date prediction of historical manuscripts and should be carefully considered. Moreover, further enhancements are possible by considering models tuned to the features and documents’ scripts.