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A Novel Text Detection System Based on Character and Link Energies.

Jing Zhang, Rangachar Kasturi

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|July 29, 2014

Summary

This study introduces a new method for text detection in images and videos using character features and a novel text model. The approach effectively identifies text objects by analyzing character properties and their relationships.

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Published on: April 1, 2016

Area of Science:

Computer Vision
Image Processing
Pattern Recognition

Background:

Text detection in images and videos is crucial for various applications.
Existing methods face challenges in accurately identifying text composed of isolated characters.

Purpose of the Study:

To develop a novel method for detecting text objects in images and videos.
To construct a new text model describing text objects as connected character units.

Main Methods:

Utilizing three new character features for text object detection.
Computing character energy based on stroke edge similarities.
Computing link energy based on inter-character similarities (color, size, etc.).
Combining energies to compute text unit energy for likelihood measurement.

Main Results:

The proposed method was evaluated on ICDAR 2003/2005, Microsoft Street View, and VACE video datasets.
Experimental results demonstrate effective text discrimination from other objects.
The method successfully captures inherent character properties.

Conclusions:

The novel text detection method is effective in various datasets.
The approach accurately identifies text objects by analyzing character and link properties.
This method offers improved text detection capabilities for images and videos.