Linear-time connected-component labeling based on sequential local operations
Computer Vision and Image Understanding
Locating Uniform-Colored Text in Video Frames
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Text Detection in Images Based on Unsupervised Classification of High-Frequency Wavelet Coefficients
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
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
A stroke filter and its application to text localization
Pattern Recognition Letters
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
A Gradient Difference Based Technique for Video Text Detection
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
A new approach for overlay text detection and extraction from complex video scene
IEEE Transactions on Image Processing
A novel text detection and localization method based on corner response
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Video text detection based on filters and edge features
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A video text location method based on background classification
International Journal on Document Analysis and Recognition
Text stream clustering algorithm based on adaptive feature selection
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A multi-layer text classification framework based on two-level representation model
Expert Systems with Applications: An International Journal
Display text segmentation after learning best-fitted OCR binarization parameters
Expert Systems with Applications: An International Journal
A new text detection algorithm in images/video frames
PCM'04 Proceedings of the 5th Pacific Rim Conference on Advances in Multimedia Information Processing - Volume Part II
A comprehensive method for multilingual video text detection, localization, and extraction
IEEE Transactions on Circuits and Systems for Video Technology
Hi-index | 12.05 |
A new method for detecting text in video images is proposed in this article. Variations in background complexity, font size and color, make detecting text regions in video images a difficult task. A pyramidal scheme is utilized to solve these problems. First, two downsized images are generated by bilinear interpolation from the original image. Then, the gradient difference of each pixel is calculated for three differently sized images, including the original one. Next, three K-means clustering procedures are applied to separate all the pixels of the three gradient difference images into two clusters: text and non-text, separately. The K-means clustering results are then combined to form the text regions. Thereafter, projection profile analysis is applied to the Sobel edge map of each text region to determine the boundaries of candidate text regions. Finally, we identify text candidates through two verification phases. In the first verification phase, we verify the geometrical properties and texture of each text candidate. In the second verification phase, statistical characteristics of the text candidate are computed using a discrete wavelet transform, and then the principal component analysis is further used to reduce the number of dimensions of these features. Next, the optimal decision function of the support vector machine, obtained by sequential minimal optimization, is applied to determine whether the text candidates contain texts or not.