KhmerST: A Low-Resource Khmer Scene Text Detection and Recognition Benchmark

• URL: http://arxiv.org/abs/2410.18277v1
• Date: Wed, 23 Oct 2024 21:04:24 GMT
• Title: KhmerST: A Low-Resource Khmer Scene Text Detection and Recognition Benchmark
• Authors: Vannkinh Nom, Souhail Bakkali, Muhammad Muzzamil Luqman, Mickaël Coustaty, Jean-Marc Ogier,
• Abstract summary: We introduce the first Khmer scene-text dataset, featuring 1,544 expert-annotated images. This diverse dataset includes flat text, raised text, poorly illuminated text, distant polygon and partially obscured text.
• Score: 1.5409800688911346
• License:
• Abstract: Developing effective scene text detection and recognition models hinges on extensive training data, which can be both laborious and costly to obtain, especially for low-resourced languages. Conventional methods tailored for Latin characters often falter with non-Latin scripts due to challenges like character stacking, diacritics, and variable character widths without clear word boundaries. In this paper, we introduce the first Khmer scene-text dataset, featuring 1,544 expert-annotated images, including 997 indoor and 547 outdoor scenes. This diverse dataset includes flat text, raised text, poorly illuminated text, distant and partially obscured text. Annotations provide line-level text and polygonal bounding box coordinates for each scene. The benchmark includes baseline models for scene-text detection and recognition tasks, providing a robust starting point for future research endeavors. The KhmerST dataset is publicly accessible at https://gitlab.com/vannkinhnom123/khmerst.

Related papers

• Dataset and Benchmark for Urdu Natural Scenes Text Detection, Recognition and Visual Question Answering [50.52792174648067]
  This initiative seeks to bridge the gap between textual and visual comprehension. We propose a new multi-task Urdu scene text dataset comprising over 1000 natural scene images. We provide fine-grained annotations for text instances, addressing the limitations of previous datasets.
  arXiv  Detail & Related papers  (2024-05-21T06:48:26Z)
• The First Swahili Language Scene Text Detection and Recognition Dataset [55.83178123785643]
  There is a significant gap in low-resource languages, especially the Swahili Language. Swahili is widely spoken in East African countries but is still an under-explored language in scene text recognition. We propose a comprehensive dataset of Swahili scene text images and evaluate the dataset on different scene text detection and recognition models.
  arXiv  Detail & Related papers  (2024-05-19T03:55:02Z)
• TEXTRON: Weakly Supervised Multilingual Text Detection through Data Programming [21.88026116276415]
  Text detection is a challenging problem in the field of computer vision (CV) There is a scarcity of word-level labeled data for text detection, especially for multilingual settings and Indian scripts. We propose TEXTRON, a Data Programming-based approach, where users can plug various text detection methods into a weak supervision-based learning framework.
  arXiv  Detail & Related papers  (2024-02-15T09:18:18Z)
• Leveraging machine learning for less developed languages: Progress on Urdu text detection [0.76146285961466]
  We present the use of machine learning methods to perform detection of Urdu text from the scene images. To support research on Urdu text, We aim to make the data freely available for research use.
  arXiv  Detail & Related papers  (2022-09-28T12:00:34Z)
• 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)
• 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)
• Urdu text in natural scene images: a new dataset and preliminary text detection [3.070994681743188]
  This work introduces a new dataset for Urdu text in natural scene images. The dataset comprises of 500 standalone images acquired from real scenes. MSER method is applied to extract Urdu text regions as candidates in an image.
  arXiv  Detail & Related papers  (2021-09-16T15:41:50Z)
• Scene Text Detection with Scribble Lines [59.698806258671105]
  We propose to annotate texts by scribble lines instead of polygons for text detection. It is a general labeling method for texts with various shapes and requires low labeling costs. Experiments show that the proposed method bridges the performance gap between the weakly labeling method and the original polygon-based labeling methods.
  arXiv  Detail & Related papers  (2020-12-09T13:14:53Z)
• AE TextSpotter: Learning Visual and Linguistic Representation for Ambiguous Text Spotting [98.08853679310603]
  This work proposes a novel text spotter, named Ambiguity Eliminating Text Spotter (AE TextSpotter) AE TextSpotter learns both visual and linguistic features to significantly reduce ambiguity in text detection. To our knowledge, it is the first time to improve text detection by using a language model.
  arXiv  Detail & Related papers  (2020-08-03T08:40:01Z)

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.