Historical Handwritten Document Age Identification using DCT-based Frequency Analysis

Abstract: Historical handwritten documents hold invaluable information about the development of language, literature, governance, education and cultural heritage. The automatic age estimation of such documents has become a major research question in digital humanities, archival science and forensic document analysis. In this paper, we propose a frequency-domain approach for age estimation of historical handwritten documents based on the Discrete Cosine Transform (DCT). The proposed framework starts with the preprocessing to minimize the scanning noise and normalize the document images. Each document image is divided into non-overlapping blocks and the 2D DCT is applied to derive frequency coefficients which represent the structural features of the handwriting and paper texture. To create discriminative feature vectors, we employ the statistical descriptors of certain DCT coefficients. SVM classifiers are used to categorize these features. Using the publicly accessible Medieval Paleographic Scale (MPS) dataset, which comprises handwritten manuscripts from several historical eras, we assess the suggested method. The findings of the experiment demonstrate that frequency features based on DCT may reliably classify ancient manuscripts and capture their age-related characteristics.

Keywords: Historical document, DCT, MPS, Document age estimation, SVM.