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45%Niche pick
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Real In, Real Out: What If We Only Use Real Data for Scene Text Editing?

Xingsong Ye, Yongkun Du, Jiaxin Zhang, Chong Sun and 1 more

Sydney Poster Session 4, Wed, Dec 9, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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lenient 0/5
medium 0/10
strict 0/5
57%Worth a look
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NeuroInk: Retinomorphic Spiking Sequence Modeling for Handwritten Text Recognition

Xiubo Liang, Jinxing Han, Yuke Li, Hongyi Duan and 4 more

Sydney Poster Session 3, Wed, Dec 9, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026

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1/20 AI panelreviewers recommend it

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AI panel: 1 of 20 reviewers recommend it
lenient 1/5
medium 0/10
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89%Must read
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Handwritten Text Recognition Lives in the High-Pixel Variance Subspace

For handwritten text recognition, discriminative signals lie in high-variance pixel directions, so pixel-reconstruction self-supervised pretraining outperforms contrastive methods and achieves lower character error rates across benchmarks.

Carlos Garrido, Jorge Calvo-Zaragoza

Paris Poster Session 3, Thu, Dec 10, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 4/5
medium 9/10
strict 3/5
80%Must read
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Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training

Transcoda uses synthetic training, normalized kern encodings, and grammar-based decoding to achieve state-of-the-art zero-shot optical music recognition with a small model.

Daniel Dratschuk, Paul Swoboda

Atlanta Poster Session 6, Fri, Dec 11, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 6/10
strict 1/5