Neural network-based text location in color images
Pattern Recognition Letters
Robust Detection of Stylized Text Events in Digital Video
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
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
An Efficient Edge Based Technique for Text Detection in Video Frames
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
A Robust Wavelet Transform Based Technique 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
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
Multi-script and multi-oriented text localization from scene images
CBDAR'11 Proceedings of the 4th international conference on Camera-Based Document Analysis and Recognition
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In this paper, we present a new method based on wavelet-median-moments and a novel idea of angle projection for detecting multi-oriented text in video. The proposed method uses wavelet decomposition first to obtain three high frequency sub-bands (LH, HL and HH) and then median moments are computed on the average sub-bands of the three high frequency sub-bands to brighten the text pixels. K-means clustering (K=2) is used for obtaining text pixels from the wavelet-median-moments features (WMMF). Text candidates are obtained by mapping the output of K-means on Sobel edge map of the original input frame. To deal with multi-oriented text, we introduce a new idea of Angle Projection (AP) based on boundary growing and nearest neighbor concepts from the text candidates instead of conventional projection profiles. The proposed method is experimented on horizontal text data, non-horizontal text data, temporal data, non-text data and camera based images (scene text data of ICDAR 2003 competition) to show that the proposed method is superior to existing methods.