mlsanityData to 14 Oct 2026

Aggregated Text Transformer for Scene Text Detection

arXiv 2211.13984 ↗First posted 25 Nov 2022cs.CVcited by 0

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Abstract (arXiv)

This paper explores the multi-scale aggregation strategy for scene text detection in natural images. We present the Aggregated Text TRansformer(ATTR), which is designed to represent texts in scene images with a multi-scale self-attention mechanism. Starting from the image pyramid with multiple resolutions, the features are first extracted at different scales with shared weight and then fed into an encoder-decoder architecture of Transformer. The multi-scale image representations are robust and contain rich information on text contents of various sizes. The text Transformer aggregates these features to learn the interaction across different scales and improve text representation. The proposed method detects scene texts by representing each text instance as an individual binary mask, which is tolerant of curve texts and regions with dense instances. Extensive experiments on public scene text detection datasets demonstrate the effectiveness of the proposed framework.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 50 of its 63 references that are in the map. First posted on arXiv 25 Nov 2022.

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References · 50

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38 more
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