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Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning

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Document Analysis and Recognition - ICDAR 2023 (ICDAR 2023)

Abstract

Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds.

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Acknowledgements

This work has been supported by grants PDC2021-121512-I00, PID2020-116298GB-I00 and PLEC2021-007850 funded by the European Union NextGenerationEU/PRTR and MCIN/AEI/10.13039/501100011033; the EU Lighthouse on Safe and Secure AI - ELSA funded by European Union’s Horizon Europe programme under grant agreement No 101070617; the Spanish Project NEOTEC SNEO-20211172 from CDTI; grant Torres Quevedo PTQ2019-010662; and the Industrial Doctorate programme of the Catalan Government (2020 DI 058).

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Correspondence to Sergi Garcia-Bordils .

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Garcia-Bordils, S., Karatzas, D., Rusiñol, M. (2023). Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning. In: Fink, G.A., Jain, R., Kise, K., Zanibbi, R. (eds) Document Analysis and Recognition - ICDAR 2023. ICDAR 2023. Lecture Notes in Computer Science, vol 14192. Springer, Cham. https://doi.org/10.1007/978-3-031-41731-3_7

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  • DOI: https://doi.org/10.1007/978-3-031-41731-3_7

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