Handwriting analysis is conducted through the expertise of Forensic Document Examiners (FDEs) by visually comparing writing samples. Through their training and years of experience, FDEs are able to recognize critical characteristics of writing to evaluate the evidence of writership. In recent years, there have been incentives to further investigate how to quantify the similarity between two written documents to support the conclusions drawn by FDEs. One way to extract information from these documents is to define various features within handwritten samples. Using an automatic algorithm within the ‘handwriter’ package in R, a sample can split into “graphs”, which are small units of writing. These graphs are sorted into 40 exemplar groups or “clusters”. The clusters consist of graphs that have similar structures found in documents throughout a database with many writers. The number of graphs per cluster for each document written by a given person acts as a quantitative feature of the handwriting. This project aims to use experimental data collected at CSAFE to study how to assess source-level hypotheses and obtain “weight-of-evidence” summaries to support examiner conclusions. Specifically, statistical models can be used to compute Bayes factors, likelihood ratios, and four different types of score-based likelihood ratios. Common source score-based likelihood ratios are explored, along with trace-anchored, source-anchored, or general-match specific source varieties, using machine learning algorithms with high discriminating capabilities.