Automatic Writer Identification in Historical Documents: A Case Study

Abstract

In recent years, Automatic Writer Identification (AWI) has received a lot of attention in the document analysis community. However, most research has been conducted on contemporary benchmark sets. These datasets typically do not contain any noise or artefacts caused by the conversion methodology. This article analyses how current state-of-the-art methods in writer identification perform on historical documents. In contrast to contemporary documents, historical data often contain artefacts such as holes, rips, or water stains which make reliable identification error-prone. Experiments were conducted on two large letter collections with known authenticity and promising results of 82% and 89% TOP-1 accuracy were achieved.

Keywords

• mustererkennung
• automatische klassifikation
• handschrift (grafologie)
• computergestütztes verfahren