Abstract
Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.
Supported by the DECRYPT Project (grant 2018-0607).
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Acknowledgements
This work has been partially supported by the Swedish Research Council (grant 2018-06074, DECRYPT), the Spanish projects PID2021-126808OB-I00 (GRAIL) and CNS2022-135947 (DOLORES), as well as the AGAUR Joan Oró FI grant 2023 FI-1-00324. The authors acknowledge the support of the Generalitat de Catalunya CERCA Program to CVC’s general activities.
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Torras, P., Souibgui, M.A., Chen, J., Biswas, S., Fornés, A. (2023). Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images. In: Coustaty, M., Fornés, A. (eds) Document Analysis and Recognition – ICDAR 2023 Workshops. ICDAR 2023. Lecture Notes in Computer Science, vol 14193. Springer, Cham. https://doi.org/10.1007/978-3-031-41498-5_6
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