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
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
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In this paper, a novel approach for detection of text and non-text regions in video frames is proposed. The proposed approach performs block wise eigen analysis on the gradient image of the video frame. For each block of the gradient frame, the dominant eigen value is computed to decide if the block could be a candidate text block. The K-means clustering is then applied to further identify text blocks among the candidate blocks. From each of the identified candidate text blocks edges are extracted using the sobel operator, and then by the use of horizontal and vertical profiles a bounding rectangle is fixed up. Further, geometric properties of the identified text regions are studied to eliminate false text regions. In order to validate the efficacy of the proposed approach, experimentation on a dataset containing 800 video frames has been carried out. The obtained results ensure that the proposed approach is with increased text detection rate with very low false and misdetection rates when compared to the other existing state of the art techniques.