Neural network-based text location in color images
Pattern Recognition Letters
Extracting Text from WWW Images
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Identification of Text on Colored Book and Journal Covers
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Locating text in complex color images
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Automatic text detection and removal in video sequences
Pattern Recognition Letters
Hybrid approach to efficient text extraction in complex color images
Pattern Recognition Letters
Text Detection in Images Based on Unsupervised Classification of Edge-based Features
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Extraction of Text Objects in Video Documents: Recent Progress
DAS '08 Proceedings of the 2008 The Eighth IAPR International Workshop on Document Analysis Systems
A Laplacian Method for Video Text Detection
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Fast and robust text detection in images and video frames
Image and Vision Computing
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In this paper, we propose a method based on the skeletonization operation for multi-oriented video text detection. The first step uses our existing Laplacian-based method to identify candidate text regions. In the second step, each region is classified as either a simple connected component (a single text string) or a complex connected component (multiple text strings that are connected to each other) depending on the number of intersection points in its skeleton. Complex connected components are then segmented into constituent parts based on the skeleton segments in order to separate the text strings from each other. Finally, text string straightness and edge density are used for false positive elimination. Experimental results show that the proposed method is able to detect multi-oriented graphics text and scene text.