DGST : Discriminator Guided Scene Text detector

• URL: http://arxiv.org/abs/2002.12509v1
• Date: Fri, 28 Feb 2020 01:47:36 GMT
• Title: DGST : Discriminator Guided Scene Text detector
• Authors: Jinyuan Zhao and Yanna Wang and Baihua Xiao and Cunzhao Shi and Fuxi Jia and Chunheng Wang
• Abstract summary: This paper proposes a detector framework based on the conditional generative adversarial networks to improve the segmentation effect of scene text detection. Experiments on standard datasets demonstrate that the proposed D GST brings noticeable gain and outperforms state-of-the-art methods.
• Score: 11.817428636084305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene text detection task has attracted considerable attention in computer vision because of its wide application. In recent years, many researchers have introduced methods of semantic segmentation into the task of scene text detection, and achieved promising results. This paper proposes a detector framework based on the conditional generative adversarial networks to improve the segmentation effect of scene text detection, called DGST (Discriminator Guided Scene Text detector). Instead of binary text score maps generated by some existing semantic segmentation based methods, we generate a multi-scale soft text score map with more information to represent the text position more reasonably, and solve the problem of text pixel adhesion in the process of text extraction. Experiments on standard datasets demonstrate that the proposed DGST brings noticeable gain and outperforms state-of-the-art methods. Specifically, it achieves an F-measure of 87% on ICDAR 2015 dataset.

Related papers

• EAFormer: Scene Text Segmentation with Edge-Aware Transformers [56.15069996649572]
  Scene text segmentation aims at cropping texts from scene images, which is usually used to help generative models edit or remove texts. We propose Edge-Aware Transformers, EAFormer, to segment texts more accurately, especially at the edge of texts.
  arXiv  Detail & Related papers  (2024-07-24T06:00:33Z)
• Text Region Multiple Information Perception Network for Scene Text Detection [19.574306663095243]
  This paper proposes a plug-and-play module called the Region Multiple Information Perception Module (RMIPM) to enhance the detection performance of segmentation-based algorithms. Specifically, we design an improved module that can perceive various types of information about scene text regions, such as text foreground classification maps, distance maps, direction maps, etc.
  arXiv  Detail & Related papers  (2024-01-18T14:36:51Z)
• Adaptive Segmentation Network for Scene Text Detection [0.0]
  We propose to automatically learn the discriminate segmentation threshold, which distinguishes text pixels from background pixels for segmentation-based scene text detectors. Besides, we design a Global-information Enhanced Feature Pyramid Network (GE-FPN) for capturing text instances with macro size and extreme aspect ratios. Finally, together with the proposed threshold learning strategy and text detection structure, we design an Adaptive Network (ASNet) for scene text detection.
  arXiv  Detail & Related papers  (2023-07-27T17:37:56Z)
• 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)
• Weakly-Supervised Text Instance Segmentation [44.20745377169349]
  We take the first attempt to perform weakly-supervised text instance segmentation by bridging text recognition and text segmentation. The proposed method significantly outperforms weakly-supervised instance segmentation methods on ICDAR13-FST (18.95$%$ improvement) and TextSeg (17.80$%$ improvement) benchmarks.
  arXiv  Detail & Related papers  (2023-03-20T03:56:47Z)
• Text Detection & Recognition in the Wild for Robot Localization [1.52292571922932]
  We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions.
  arXiv  Detail & Related papers  (2022-05-17T18:16:34Z)
• Towards End-to-End Unified Scene Text Detection and Layout Analysis [60.68100769639923]
  We introduce the task of unified scene text detection and layout analysis. The first hierarchical scene text dataset is introduced to enable this novel research task. We also propose a novel method that is able to simultaneously detect scene text and form text clusters in a unified way.
  arXiv  Detail & Related papers  (2022-03-28T23:35:45Z)
• SwinTextSpotter: Scene Text Spotting via Better Synergy between Text Detection and Text Recognition [73.61592015908353]
  We propose a new end-to-end scene text spotting framework termed SwinTextSpotter. Using a transformer with dynamic head as the detector, we unify the two tasks with a novel Recognition Conversion mechanism. The design results in a concise framework that requires neither additional rectification module nor character-level annotation.
  arXiv  Detail & Related papers  (2022-03-19T01:14:42Z)
• Attention-based Feature Decomposition-Reconstruction Network for Scene Text Detection [20.85468268945721]
  We propose attention-based feature decomposition-reconstruction network for scene text detection. We use contextual information and low-level feature to enhance the performance of segmentation-based text detector. Experiments have been conducted on two public benchmark datasets and results show that our proposed method achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2021-11-29T06:15:25Z)
• MOST: A Multi-Oriented Scene Text Detector with Localization Refinement [67.35280008722255]
  We propose a new algorithm for scene text detection, which puts forward a set of strategies to significantly improve the quality of text localization. Specifically, a Text Feature Alignment Module (TFAM) is proposed to dynamically adjust the receptive fields of features. A Position-Aware Non-Maximum Suppression (PA-NMS) module is devised to exclude unreliable ones.
  arXiv  Detail & Related papers  (2021-04-02T14:34:41Z)
• Text Perceptron: Towards End-to-End Arbitrary-Shaped Text Spotting [49.768327669098674]
  We propose an end-to-end trainable text spotting approach named Text Perceptron. It first employs an efficient segmentation-based text detector that learns the latent text reading order and boundary information. Then a novel Shape Transform Module (abbr. STM) is designed to transform the detected feature regions into regular morphologies.
  arXiv  Detail & Related papers  (2020-02-17T08:07:19Z)

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.