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Related Experiment Video

Updated: Jun 9, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

A Laplacian approach to multi-oriented text detection in video.

Palaiahnakote Shivakumara¹, Trung Quy Phan, Chew Lim Tan

¹Department of Computer Science, School of Computing, National University of Singapore, Singapore. shiva@comp.nus.edu.sg

IEEE Transactions on Pattern Analysis and Machine Intelligence

|August 25, 2010

Summary

This study introduces a novel frequency domain method for video text detection that accurately identifies text regardless of its orientation. The approach effectively handles both graphics and scene text, improving upon methods limited to horizontal text.

Related Experiment Videos

Last Updated: Jun 9, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Traditional video text detection methods often struggle with text in non-horizontal orientations.
Detecting text in diverse scenarios, including graphics and natural scenes, remains a challenge.

Purpose of the Study:

To develop a robust video text detection method capable of handling text with arbitrary orientations.
To improve the accuracy and applicability of text detection in complex visual environments.

Main Methods:

Utilizing the Laplacian in the frequency domain for image filtering (Fourier-Laplacian).
Employing K-means clustering to identify candidate text regions based on maximum difference.
Leveraging connected component skeletons to differentiate text strings.
Implementing text string straightness and edge density for false positive elimination.

Main Results:

The proposed method successfully detects text in both graphics and scene images.
It demonstrates effectiveness in identifying text regardless of its horizontal or non-horizontal orientation.
Experimental results validate the robustness of the approach across various text types and orientations.

Conclusions:

The frequency domain Laplacian-based method offers a significant advancement in video text detection.
This technique provides a versatile solution for handling arbitrarily oriented text, outperforming existing methods.
The approach is well-suited for real-world applications involving diverse text elements in videos.