SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild

• URL: http://arxiv.org/abs/2501.02962v2
• Date: Tue, 07 Jan 2025 02:51:31 GMT
• Title: SceneVTG++: Controllable Multilingual Visual Text Generation in the Wild
• Authors: Jiawei Liu, Yuanzhi Zhu, Feiyu Gao, Zhibo Yang, Peng Wang, Junyang Lin, Xinggang Wang, Wenyu Liu,
• Abstract summary: The text in natural scene images needs to meet the following four key criteria. The generated text can facilitate to the training of natural scene OCR (Optical Character Recognition) tasks. The generated images have superior utility in OCR tasks like text detection and text recognition.
• Score: 55.619708995575785
• License:
• Abstract: Generating visual text in natural scene images is a challenging task with many unsolved problems. Different from generating text on artificially designed images (such as posters, covers, cartoons, etc.), the text in natural scene images needs to meet the following four key criteria: (1) Fidelity: the generated text should appear as realistic as a photograph and be completely accurate, with no errors in any of the strokes. (2) Reasonability: the text should be generated on reasonable carrier areas (such as boards, signs, walls, etc.), and the generated text content should also be relevant to the scene. (3) Utility: the generated text can facilitate to the training of natural scene OCR (Optical Character Recognition) tasks. (4) Controllability: The attribute of the text (such as font and color) should be controllable as needed. In this paper, we propose a two stage method, SceneVTG++, which simultaneously satisfies the four aspects mentioned above. SceneVTG++ consists of a Text Layout and Content Generator (TLCG) and a Controllable Local Text Diffusion (CLTD). The former utilizes the world knowledge of multi modal large language models to find reasonable text areas and recommend text content according to the nature scene background images, while the latter generates controllable multilingual text based on the diffusion model. Through extensive experiments, we respectively verified the effectiveness of TLCG and CLTD, and demonstrated the state-of-the-art text generation performance of SceneVTG++. In addition, the generated images have superior utility in OCR tasks like text detection and text recognition. Codes and datasets will be available.

Related papers

• CLII: Visual-Text Inpainting via Cross-Modal Predictive Interaction [23.683636588751753]
  State-of-the-art inpainting methods are mainly designed for nature images and cannot correctly recover text within scene text images. We identify the visual-text inpainting task to achieve high-quality scene text image restoration and text completion.
  arXiv  Detail & Related papers  (2024-07-23T06:12:19Z)
• Visual Text Generation in the Wild [67.37458807253064]
  We propose a visual text generator (termed SceneVTG) which can produce high-quality text images in the wild. The proposed SceneVTG significantly outperforms traditional rendering-based methods and recent diffusion-based methods in terms of fidelity and reasonability. The generated images provide superior utility for tasks involving text detection and text recognition.
  arXiv  Detail & Related papers  (2024-07-19T09:08:20Z)
• Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model [31.819060415422353]
  Diff-Text is a training-free scene text generation framework for any language. Our method outperforms the existing method in both the accuracy of text recognition and the naturalness of foreground-background blending.
  arXiv  Detail & Related papers  (2023-12-19T15:18:40Z)
• 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)
• AnyText: Multilingual Visual Text Generation And Editing [18.811943975513483]
  We introduce AnyText, a diffusion-based multilingual visual text generation and editing model. AnyText can write characters in multiple languages, to the best of our knowledge, this is the first work to address multilingual visual text generation. We contribute the first large-scale multilingual text images dataset, AnyWord-3M, containing 3 million image-text pairs with OCR annotations in multiple languages.
  arXiv  Detail & Related papers  (2023-11-06T12:10:43Z)
• Conditional Text Image Generation with Diffusion Models [18.017541111064602]
  We propose a method called Text Image Generation with Conditional Models (CTIG-DM) Four text image generation modes, namely: synthesis mode, augmentation mode, recovery mode, and imitation mode, can be derived by combining and configuring these three conditions. CTIG-DM is able to produce image samples that simulate real-world complexity and diversity, and thus can boost the performance of existing text recognizers.
  arXiv  Detail & Related papers  (2023-06-19T09:44:43Z)
• TextDiffuser: Diffusion Models as Text Painters [118.30923824681642]
  We introduce TextDiffuser, focusing on generating images with visually appealing text that is coherent with backgrounds. We contribute the first large-scale text images dataset with OCR annotations, MARIO-10M, containing 10 million image-text pairs. We show that TextDiffuser is flexible and controllable to create high-quality text images using text prompts alone or together with text template images, and conduct text inpainting to reconstruct incomplete images with text.
  arXiv  Detail & Related papers  (2023-05-18T10:16:19Z)
• Exploring Stroke-Level Modifications for Scene Text Editing [86.33216648792964]
  Scene text editing (STE) aims to replace text with the desired one while preserving background and styles of the original text. Previous methods of editing the whole image have to learn different translation rules of background and text regions simultaneously. We propose a novel network by MOdifying Scene Text image at strokE Level (MOSTEL)
  arXiv  Detail & Related papers  (2022-12-05T02:10:59Z)
• Language Matters: A Weakly Supervised Pre-training Approach for Scene Text Detection and Spotting [69.77701325270047]
  This paper presents a weakly supervised pre-training method that can acquire effective scene text representations. Our network consists of an image encoder and a character-aware text encoder that extract visual and textual features. Experiments show that our pre-trained model improves F-score by +2.5% and +4.8% while transferring its weights to other text detection and spotting networks.
  arXiv  Detail & Related papers  (2022-03-08T08:10:45Z)

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.