A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond

• URL: http://arxiv.org/abs/2411.11354v1
• Date: Mon, 18 Nov 2024 07:50:22 GMT
• Title: A comprehensive survey of oracle character recognition: challenges, benchmarks, and beyond
• Authors: Jing Li, Xueke Chi, Qiufeng Wang, Dahan Wang, Kaizhu Huang, Yongge Liu, Cheng-lin Liu,
• Abstract summary: Oracle character recognition has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition.
• Score: 39.542007188857376
• License:
• Abstract: Oracle character recognition-an analysis of ancient Chinese inscriptions found on oracle bones-has become a pivotal field intersecting archaeology, paleography, and historical cultural studies. Traditional methods of oracle character recognition have relied heavily on manual interpretation by experts, which is not only labor-intensive but also limits broader accessibility to the general public. With recent breakthroughs in pattern recognition and deep learning, there is a growing movement towards the automation of oracle character recognition (OrCR), showing considerable promise in tackling the challenges inherent to these ancient scripts. However, a comprehensive understanding of OrCR still remains elusive. Therefore, this paper presents a systematic and structured survey of the current landscape of OrCR research. We commence by identifying and analyzing the key challenges of OrCR. Then, we provide an overview of the primary benchmark datasets and digital resources available for OrCR. A review of contemporary research methodologies follows, in which their respective efficacies, limitations, and applicability to the complex nature of oracle characters are critically highlighted and examined. Additionally, our review extends to ancillary tasks associated with OrCR across diverse disciplines, providing a broad-spectrum analysis of its applications. We conclude with a forward-looking perspective, proposing potential avenues for future investigations that could yield significant advancements in the field.

Related papers

• On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey [82.49623756124357]
  Zero-shot image recognition (ZSIR) aims at empowering models to recognize and reason in unseen domains. This paper presents a broad review of recent advances in element-wise ZSIR. We first attempt to integrate the three basic ZSIR tasks of object recognition, compositional recognition, and foundation model-based open-world recognition into a unified element-wise perspective.
  arXiv  Detail & Related papers  (2024-08-09T05:49:21Z)
• Self-Supervised Learning for Text Recognition: A Critical Survey [11.599791967838481]
  Text Recognition (TR) refers to the research area that focuses on retrieving textual information from images. Self-Supervised Learning (SSL) has gained attention by utilizing large datasets of unlabeled data to train Deep Neural Networks (DNN) This paper seeks to consolidate the use of SSL in the field of TR, offering a critical and comprehensive overview of the current state of the art.
  arXiv  Detail & Related papers  (2024-07-29T11:11:17Z)
• Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models [52.24001776263608]
  This comprehensive survey delves into the recent strides in HS moderation. We highlight the burgeoning role of large language models (LLMs) and large multimodal models (LMMs) We identify existing gaps in research, particularly in the context of underrepresented languages and cultures.
  arXiv  Detail & Related papers  (2024-01-30T03:51:44Z)
• Advancements and Challenges in Arabic Optical Character Recognition: A Comprehensive Survey [0.6629765271909505]
  This paper seeks to offer an exhaustive review of contemporary applications, methodologies, and challenges associated with Arabic Optical Character Recognition (OCR) A thorough analysis is conducted on prevailing techniques utilized throughout the OCR process, with a dedicated effort to discern the most efficacious approaches that demonstrate enhanced outcomes. In addition to presenting cutting-edge techniques and methods, this paper critically identifies research gaps within the realm of Arabic OCR.
  arXiv  Detail & Related papers  (2023-12-19T03:01:31Z)
• Oracle Character Recognition using Unsupervised Discriminative Consistency Network [65.64172835624206]
  We propose a novel unsupervised domain adaptation method for oracle character recognition (OrCR) We leverage pseudo-labeling to incorporate the semantic information into adaptation and constrain augmentation consistency. Our approach achieves state-of-the-art result on Oracle-241 dataset and substantially outperforms the recently proposed structure-texture separation network by 15.1%.
  arXiv  Detail & Related papers  (2023-12-11T02:52:27Z)
• OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models [122.27878464009181]
  We conducted a comprehensive evaluation of Large Multimodal Models, such as GPT4V and Gemini, in various text-related visual tasks. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available.
  arXiv  Detail & Related papers  (2023-05-13T11:28:37Z)
• Review of coreference resolution in English and Persian [8.604145658574689]
  Coreference resolution (CR) identifies expressions referring to the same real-world entity. This paper explores the latest advancements in CR, spanning coreference and anaphora resolution. Recognizing the unique challenges of Persian CR, we dedicate a focused analysis to this under-resourced language.
  arXiv  Detail & Related papers  (2022-11-08T18:14:09Z)
• Deep Learning meets Liveness Detection: Recent Advancements and Challenges [3.2011056280404637]
  We present a comprehensive survey on the literature related to deep-feature-based FAS methods since 2017. We cover predominant public datasets for FAS in chronological order, their evolutional progress, and the evaluation criteria.
  arXiv  Detail & Related papers  (2021-12-29T19:24:58Z)
• On how Cognitive Computing will plan your next Systematic Review [3.0816257225447763]
  We report on the insights from 24 SLR authors on planning practices, its challenges and feedback on support strategies. We frame our findings under the cognitive augmentation framework, and report on a prototype implementation and evaluation.
  arXiv  Detail & Related papers  (2020-12-15T09:56:09Z)
• Survey on the Analysis and Modeling of Visual Kinship: A Decade in the Making [66.72253432908693]
  Kinship recognition is a challenging problem with many practical applications. We review the public resources and data challenges that enabled and inspired many to hone-in on the views. For the tenth anniversary, the demo code is provided for the various kin-based tasks.
  arXiv  Detail & Related papers  (2020-06-29T13:25:45Z)

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.