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GLASS: Global to Local Attention for Scene-Text Spotting

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13688))

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Abstract

In recent years, the dominant paradigm for text spotting is to combine the tasks of text detection and recognition into a single end-to-end framework. Under this paradigm, both tasks are accomplished by operating over a shared global feature map extracted from the input image. Among the main challenges that end-to-end approaches face is the performance degradation when recognizing text across scale variations (smaller or larger text), and arbitrary word rotation angles. In this work, we address these challenges by proposing a novel global-to-local attention mechanism for text spotting, termed GLASS, that fuses together global and local features. The global features are extracted from the shared backbone, preserving contextual information from the entire image, while the local features are computed individually on resized, high resolution rotated word crops. The information extracted from the local crops alleviates much of the inherent difficulties with scale and word rotation. We show a performance analysis across scales and angles, highlighting improvement over scale and angle extremities. In addition, we introduce an orientation-aware loss term supervising the detection task, and show its contribution to both detection and recognition performance across all angles. Finally, we show that GLASS is general by incorporating it into other leading text spotting architectures, improving their text spotting performance. Our method achieves state-of-the-art results on multiple benchmarks, including the newly released TextOCR.

R. Ronen and S. Tsiper—Equal contibution.

Code available at https://www.github.com/amazon-research/glass-text-spotting.

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Notes

  1. 1.

    https://github.com/MhLiao/MaskTextSpotterV3.

  2. 2.

    https://github.com/aim-uofa/AdelaiDet.

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Author information

Authors and Affiliations

  1. Viterbi Faculty of Electrical and Computer Engineering, Technion, Haifa, Israel

    Roi Ronen

  2. AWS AI Labs, Tel Aviv, Israel

    Shahar Tsiper, Oron Anschel, Inbal Lavi & Amir Markovitz

  3. AWS AI Labs, San Francisco, CA, USA

    R. Manmatha

Authors
  1. Roi Ronen
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  2. Shahar Tsiper
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  3. Oron Anschel
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  4. Inbal Lavi
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  5. Amir Markovitz
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  6. R. Manmatha
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Corresponding author

Correspondence to Shahar Tsiper .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Ronen, R., Tsiper, S., Anschel, O., Lavi, I., Markovitz, A., Manmatha, R. (2022). GLASS: Global to Local Attention for Scene-Text Spotting. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13688. Springer, Cham. https://doi.org/10.1007/978-3-031-19815-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-19815-1_15

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