TU Dortmund Department of Computer Science LS XII Pattern Recognition GroupPublications → Publication Details

Embedded Attributes for Cuneiform Sign Spotting

Eugen Rusakov, Turna Somel, Gerfrid G. W. Mueller and Gernot A. Fink
Proc. Int. Conf. on Document Analysis and Recognition, Lausanne, Switzerland, 2021.

In the document analysis community, intermediate represen- tations based on binary attributes are used to perform retrieval tasks or recognize unseen categories. These visual attributes representing high- level semantics continually achieve state-of-the-art results, especially for the task of word spotting. While spotting tasks are mainly performed on Latin or Arabic scripts, the cuneiform writing system is still a less well-known domain for the document analysis community. In contrast to the Latin alphabet, the cuneiform writing system consists of many different signs written by pressing a wedge stylus into moist clay tablets. Cuneiform signs are defined by different constellations and relative po- sitions of wedge impressions, which can be exploited to define sign rep- resentations based on visual attributes. A promising approach of repre- senting cuneiform sign using visual attributes is based on the so-called Gottstein-System. Here, cuneiform signs are described by counting the wedge types from a holistic perspective without any spatial information for wedge positions within a sign. We extend this holistic representation by a spatial pyramid approach with a more fine-grained description of cuneiform signs. In this way, the proposed representation is capable of describing a single sign in a more detailed way and represent a more extensive set of sign categories.

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