Abstract
In the document analysis community, intermediate representations 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 positions of wedge impressions, which can be exploited to define sign representations based on visual attributes. A promising approach of representing 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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Acknowledgements
This work is supported by the German Research Foundation (DFG) within the scope of the project Computer-unterstützte Keilschriftanalyse (CuKa).
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Rusakov, E., Somel, T., Müller, G.G.W., Fink, G.A. (2021). Embedded Attributes for Cuneiform Sign Spotting. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12822. Springer, Cham. https://doi.org/10.1007/978-3-030-86331-9_19
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DOI: https://doi.org/10.1007/978-3-030-86331-9_19
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