WAS: Dataset and Methods for Artistic Text Segmentation

• URL: http://arxiv.org/abs/2408.00106v1
• Date: Wed, 31 Jul 2024 18:29:36 GMT
• Title: WAS: Dataset and Methods for Artistic Text Segmentation
• Authors: Xudong Xie, Yuzhe Li, Yang Liu, Zhifei Zhang, Zhaowen Wang, Wei Xiong, Xiang Bai,
• Abstract summary: This paper focuses on the more challenging task of artistic text segmentation and constructs a real artistic text segmentation dataset. We propose a decoder with the layer-wise momentum query to prevent the model from ignoring stroke regions of special shapes. We also propose a skeleton-assisted head to guide the model to focus on the global structure.
• Score: 57.61335995536524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate text segmentation results are crucial for text-related generative tasks, such as text image generation, text editing, text removal, and text style transfer. Recently, some scene text segmentation methods have made significant progress in segmenting regular text. However, these methods perform poorly in scenarios containing artistic text. Therefore, this paper focuses on the more challenging task of artistic text segmentation and constructs a real artistic text segmentation dataset. One challenge of the task is that the local stroke shapes of artistic text are changeable with diversity and complexity. We propose a decoder with the layer-wise momentum query to prevent the model from ignoring stroke regions of special shapes. Another challenge is the complexity of the global topological structure. We further design a skeleton-assisted head to guide the model to focus on the global structure. Additionally, to enhance the generalization performance of the text segmentation model, we propose a strategy for training data synthesis, based on the large multi-modal model and the diffusion model. Experimental results show that our proposed method and synthetic dataset can significantly enhance the performance of artistic text segmentation and achieve state-of-the-art results on other public datasets.

Related papers

• Towards Unified Multi-granularity Text Detection with Interactive Attention [56.79437272168507]
  "Detect Any Text" is an advanced paradigm that unifies scene text detection, layout analysis, and document page detection into a cohesive, end-to-end model. A pivotal innovation in DAT is the across-granularity interactive attention module, which significantly enhances the representation learning of text instances. Tests demonstrate that DAT achieves state-of-the-art performances across a variety of text-related benchmarks.
  arXiv  Detail & Related papers  (2024-05-30T07:25:23Z)
• Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation [28.24883865053459]
  This paper aims to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations. Existing methods have demonstrated that contrastive learning on image-text pairs effectively aligns visual segments with the meanings of texts. A text often consists of multiple semantic concepts, whereas semantic segmentation strives to create semantically homogeneous segments.
  arXiv  Detail & Related papers  (2024-04-05T17:25:17Z)
• From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions [63.11097464396147]
  We introduce a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. We also introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-27T15:59:37Z)
• Enhancing Scene Text Detectors with Realistic Text Image Synthesis Using Diffusion Models [63.99110667987318]
  We present DiffText, a pipeline that seamlessly blends foreground text with the background's intrinsic features. With fewer text instances, our produced text images consistently surpass other synthetic data in aiding text detectors.
  arXiv  Detail & Related papers  (2023-11-28T06:51:28Z)
• Self-supervised Scene Text Segmentation with Object-centric Layered Representations Augmented by Text Regions [22.090074821554754]
  We propose a self-supervised scene text segmentation algorithm with layered decoupling of representations derived from the object-centric manner to segment images into texts and background. On several public scene text datasets, our method outperforms the state-of-the-art unsupervised segmentation algorithms.
  arXiv  Detail & Related papers  (2023-08-25T05:00:05Z)
• 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)
• A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution [13.934846626570286]
  Scene text image super-resolution aims to increase the resolution and readability of the text in low-resolution images. It remains difficult to reconstruct high-resolution images for spatially deformed texts, especially rotated and curve-shaped ones. We propose a CNN based Text ATTention network (TATT) to address this problem.
  arXiv  Detail & Related papers  (2022-03-17T15:28:29Z)
• Pre-training Language Model Incorporating Domain-specific Heterogeneous Knowledge into A Unified Representation [49.89831914386982]
  We propose a unified pre-trained language model (PLM) for all forms of text, including unstructured text, semi-structured text, and well-structured text. Our approach outperforms the pre-training of plain text using only 1/4 of the data.
  arXiv  Detail & Related papers  (2021-09-02T16:05:24Z)
• Rethinking Text Segmentation: A Novel Dataset and A Text-Specific Refinement Approach [34.63444886780274]
  Text segmentation is a prerequisite in real-world text-related tasks. We introduce Text Refinement Network (TexRNet), a novel text segmentation approach. TexRNet consistently improves text segmentation performance by nearly 2% compared to other state-of-the-art segmentation methods.
  arXiv  Detail & Related papers  (2020-11-27T22:50:09Z)

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.