  Standard OCR is a well-researched topic of computer vision and can be
considered solved for machine-printed text. However, when applied to
unconstrained images, the recognition rates drop drastically. Therefore, the
employment of object recognition-based techniques has become state of the art
in scene text recognition applications. This paper presents a scene text
recognition method tailored to ancient coin legends and compares the results
achieved in character and word recognition experiments to a standard OCR
engine. The conducted experiments show that the proposed method outperforms the
standard OCR engine on a set of 180 cropped coin legend words.
