  Handwritten document analysis is an area of forensic science, with the goal
of establishing authorship of documents through examination of inherent
characteristics. Law enforcement agencies use standard protocols based on
manual processing of handwritten documents. This method is time-consuming, is
often subjective in its evaluation, and is not replicable. To overcome these
limitations, in this paper we present a framework capable of extracting and
analyzing intrinsic measures of manuscript documents related to text line
heights, space between words, and character sizes using image processing and
deep learning techniques. The final feature vector for each document involved
consists of the mean and standard deviation for every type of measure
collected. By quantifying the Euclidean distance between the feature vectors of
the documents to be compared, authorship can be discerned. Our study pioneered
the comparison between traditionally handwritten documents and those produced
with digital tools (e.g., tablets). Experimental results demonstrate the
ability of our method to objectively determine authorship in different writing
media, outperforming the state of the art.
