The First Swahili Language Scene Text Detection and Recognition Dataset

• URL: http://arxiv.org/abs/2405.11437v1
• Date: Sun, 19 May 2024 03:55:02 GMT
• Title: The First Swahili Language Scene Text Detection and Recognition Dataset
• Authors: Fadila Wendigoundi Douamba, Jianjun Song, Ling Fu, Yuliang Liu, Xiang Bai,
• Abstract summary: 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.
• Score: 55.83178123785643
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Scene text recognition is essential in many applications, including automated translation, information retrieval, driving assistance, and enhancing accessibility for individuals with visual impairments. Much research has been done to improve the accuracy and performance of scene text detection and recognition models. However, most of this research has been conducted in the most common languages, English and Chinese. 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. No studies have been focused explicitly on Swahili natural scene text detection and recognition, and no dataset for Swahili language scene text detection and recognition is publicly available. We propose a comprehensive dataset of Swahili scene text images and evaluate the dataset on different scene text detection and recognition models. The dataset contains 976 images collected in different places and under various circumstances. Each image has its annotation at the word level. The proposed dataset can also serve as a benchmark dataset specific to the Swahili language for evaluating and comparing different approaches and fostering future research endeavors. The dataset is available on GitHub via this link: https://github.com/FadilaW/Swahili-STR-Dataset

Related papers

• KhmerST: A Low-Resource Khmer Scene Text Detection and Recognition Benchmark [1.5409800688911346]
  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.
  arXiv  Detail & Related papers  (2024-10-23T21:04:24Z)
• 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)
• IndicSTR12: A Dataset for Indic Scene Text Recognition [33.194567434881314]
  This paper proposes the largest and most comprehensive real dataset - IndicSTR12 - and benchmarking STR performance on 12 major Indian languages. The size and complexity of the proposed dataset are comparable to those of existing Latin contemporaries. The dataset contains over 27000 word-images gathered from various natural scenes, with over 1000 word-images for each language.
  arXiv  Detail & Related papers  (2024-03-12T18:14:48Z)
• MENTOR: Multilingual tExt detectioN TOward leaRning by analogy [59.37382045577384]
  We propose a framework to detect and identify both seen and unseen language regions inside scene images. "MENTOR" is the first work to realize a learning strategy between zero-shot learning and few-shot learning for multilingual scene text detection.
  arXiv  Detail & Related papers  (2024-03-12T03:35:17Z)
• 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)
• Towards Detecting, Recognizing, and Parsing the Address Information from Bangla Signboard: A Deep Learning-based Approach [1.3778851745408136]
  We have proposed an end-to-end system with deep learning-based models for detecting, recognizing, correcting, and parsing information from Bangla signboards. We have created manually annotated and synthetic datasets to train signboard detection, address text detection, address text recognition, and address text models. Finally, we have developed a Bangla address text using the state-of-the-art transformer-based pre-trained language model.
  arXiv  Detail & Related papers  (2023-11-22T08:25:15Z)
• Orientation-Independent Chinese Text Recognition in Scene Images [61.34060587461462]
  We take the first attempt to extract orientation-independent visual features by disentangling content and orientation information of text images. Specifically, we introduce a Character Image Reconstruction Network (CIRN) to recover corresponding printed character images with disentangled content and orientation information.
  arXiv  Detail & Related papers  (2023-09-03T05:30:21Z)
• ASR2K: Speech Recognition for Around 2000 Languages without Audio [100.41158814934802]
  We present a speech recognition pipeline that does not require any audio for the target language. Our pipeline consists of three components: acoustic, pronunciation, and language models. We build speech recognition for 1909 languages by combining it with Crubadan: a large endangered languages n-gram database.
  arXiv  Detail & Related papers  (2022-09-06T22:48:29Z)
• From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network [70.47504933083218]
  We propose a Visual Language Modeling Network (VisionLAN), which views the visual and linguistic information as a union. VisionLAN significantly improves the speed by 39% and adaptively considers the linguistic information to enhance the visual features for accurate recognition.
  arXiv  Detail & Related papers  (2021-08-22T07:56:24Z)

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.