Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

A spatial-temporal approach for video caption detection and recognition.

Xiaoou Tang1, Xinbo Gao, Jianzhuang Liu

  • 1Dept. of Inf. Eng., Chinese Univ. of Hong Kong, China.

IEEE Transactions on Neural Networks
|February 5, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Perceptual Quality Assessment of Low-Light Enhanced Images: A Multi-Annotated Subjective Dataset and a Multimodal Objective Method.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

WSformer: Wavelet-Based Sparse Transformer for Blind Image Restoration.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Interpretable General Image Fusion via Scalable Autoregressive Modeling.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Development and validation of the Pulmonary Nodule Malignant Transformation Fear Scale (PN-MTFS) to identify patients at high risk of cancer-related fear: a multicenter study.

Frontiers in psychology·2026
Same author

OutDreamer: Video Outpainting With a Diffusion Transformer.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Mitigating bias in chest X-ray disease diagnosis via de-biased disentangled representation learning.

Artificial intelligence in medicine·2026
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study introduces a novel system for detecting and recognizing Chinese video captions using a fuzzy-clustering neural network (FCNN). The system significantly improves accuracy, achieving 86% recognition for news video captions.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Video captioning is crucial for content analysis and accessibility.
  • Accurate detection and recognition of text in videos, especially Chinese characters, remain challenging.

Purpose of the Study:

  • To develop an effective system for video caption detection and recognition.
  • To enhance the accuracy of Chinese video caption recognition.

Main Methods:

  • A fuzzy-clustering neural network (FCNN) classifier was employed.
  • A novel caption-transition detection scheme was utilized for precise spatial and temporal localization.
  • Advanced character segmentation and binarization techniques were developed.

Main Results:

Related Experiment Videos

  • The system achieved high precision and efficiency in locating video captions.
  • Chinese video caption recognition accuracy was improved from 13% to 86% on news video datasets.
  • This represents a significant advancement in Chinese video caption recognition.

Conclusions:

  • The developed FCNN-based system demonstrates a highly effective approach for video caption detection and recognition.
  • The novel techniques significantly boost recognition accuracy for Chinese video captions.
  • This work provides a promising foundation for future research in multilingual video text analysis.