Do You Need Text Rectification? Soft Attention Mask Embedding for Rectification-Free Scene Text Spotting

• URL: http://arxiv.org/abs/2605.18173v1
• Date: Mon, 18 May 2026 10:15:57 GMT
• Title: Do You Need Text Rectification? Soft Attention Mask Embedding for Rectification-Free Scene Text Spotting
• Abstract summary: Same-Net is a robust end-to-end text spotting framework that requires neither character-level annotations nor auxiliary text rectification modules.<n>Same-Net achieves 84.02% end-to-end H-mean on the arbitrarily-shaped Total-Text dataset, surpassing the previous state-of-the-art GLASS by 1.02% in full-lexicon accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: End-to-end scene text spotting, which unifies text detection and recognition within a single framework, has witnessed remarkable progress driven by deep learning advances. However, most existing approaches still suffer from incomplete mask proposals caused by multi-scale variation, arbitrary text shapes, and complex background interference, thereby degrading recognition accuracy. In this paper, we propose a novel Soft Attention Mask Embedding module (SAME) that leverages the global receptive field of Transformer encoders to encode high-level features and compute soft attention weights, which are then hierarchically embedded with predicted masks to generate refined text-boundary-aware masks that effectively suppress background noise. Building upon this module, we present SAME-Net, a robust end-to-end text spotting framework that requires neither character-level annotations nor auxiliary text rectification modules. Since the soft attention mechanism is fully differentiable, recognition loss gradients can be back-propagated through the SAME module to the detection branch, enabling joint optimization of detection and recognition objectives. Extensive experiments on challenging benchmarks demonstrate the effectiveness of our approach: SAME-Net achieves 84.02\% end-to-end H-mean on the arbitrarily-shaped Total-Text dataset, surpassing the previous state-of-the-art GLASS by 1.02\% in full-lexicon accuracy without additional training data, and obtains competitive 83.4\% strong-lexicon results on the multi-oriented ICDAR 2015 dataset.

Related papers

• PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution [51.96078493242164]
  PRISM is a single-step diffusion-based Text-SR framework.<n> PRISM achieves state-of-the-art performance with millisecond-level inference.
  arXiv  Detail & Related papers  (2026-05-13T05:31:06Z)
• Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation [58.85645136534301]
  Existing watermarking schemes for sampled text often face trade-offs between maintaining text quality and ensuring robust detection against various attacks.<n>We propose a novel watermarking scheme that improves both detectability and text quality by introducing a cumulative watermark entropy threshold.
  arXiv  Detail & Related papers  (2025-04-16T14:16:38Z)
• TextSleuth: Towards Explainable Tampered Text Detection [49.88698441048043]
  We propose to explain the basis of tampered text detection with natural language via large multimodal models.<n>To fill the data gap for this task, we propose a large-scale, comprehensive dataset, ETTD.<n>Elaborate queries are introduced to generate high-quality anomaly descriptions with GPT4o.<n>To automatically filter out low-quality annotations, we also propose to prompt GPT4o to recognize tampered texts.
  arXiv  Detail & Related papers  (2024-12-19T13:10:03Z)
• Class-Aware Mask-Guided Feature Refinement for Scene Text Recognition [56.968108142307976]
  We propose a novel approach called Class-Aware Mask-guided feature refinement (CAM) Our approach introduces canonical class-aware glyph masks to suppress background and text style noise. By enhancing the alignment between the canonical mask feature and the text feature, the module ensures more effective fusion.
  arXiv  Detail & Related papers  (2024-02-21T09:22:45Z)
• Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision [87.15580604023555]
  Unpair-Seg is a novel weakly-supervised open-vocabulary segmentation framework. It learns from unpaired image-mask and image-text pairs, which can be independently and efficiently collected. It achieves 14.6% and 19.5% mIoU on the ADE-847 and PASCAL Context-459 datasets.
  arXiv  Detail & Related papers  (2024-02-14T06:01:44Z)
• Inverse-like Antagonistic Scene Text Spotting via Reading-Order Estimation and Dynamic Sampling [26.420235903805782]
  We propose a unified end-to-end trainable inverse-like antagonistic text spotting framework dubbed IATS. Specifically, we propose an innovative reading-order estimation module (REM) that extracts reading-order information from the initial text boundary. We show that our method achieves superior performance both on irregular and inverse-like text spotting.
  arXiv  Detail & Related papers  (2024-01-08T02:47:47Z)
• TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution [17.95994419104427]
  TextDiff is a diffusion-based framework tailored for scene text image super-resolution.<n>It achieves state-of-the-art (SOTA) performance on public benchmark datasets.<n>Our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods.
  arXiv  Detail & Related papers  (2023-08-13T11:02:16Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• TextDCT: Arbitrary-Shaped Text Detection via Discrete Cosine Transform Mask [19.269070203448187]
  Arbitrary-shaped scene text detection is a challenging task due to the variety of text changes in font, size, color, and orientation. We propose a novel light-weight anchor-free text detection framework called TextDCT, which adopts the discrete cosine transform (DCT) to encode the text masks as compact vectors. TextDCT achieves F-measure of 85.1 at 17.2 frames per second (FPS) and F-measure of 84.9 at 15.1 FPS for CTW1500 and Total-Text datasets, respectively.
  arXiv  Detail & Related papers  (2022-06-27T15:42:25Z)
• Adaptive Shrink-Mask for Text Detection [91.34459257409104]
  Existing real-time text detectors reconstruct text contours by shrink-masks directly. The dependence on predicted shrink-masks leads to unstable detection results. Super-pixel Window (SPW) is designed to supervise the network.
  arXiv  Detail & Related papers  (2021-11-18T07:38:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.