An introduction to digital image processing
An introduction to digital image processing
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing: A Practical Introduction Using Java (with CD-ROM)
Digital Image Processing: A Practical Introduction Using Java (with CD-ROM)
Adaptive Document Binarization
ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
Video OCR: indexing digital new libraries by recognition of superimposed captions
Multimedia Systems - Special section on video libraries
Text Detection for Video Analysis
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Automatic Performance Evaluation for Video Text Detection
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Text Extraction from Color Documents - Clustering Approaches in Three and Four Dimensions
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
A novel initialization scheme for the fuzzy c-means algorithm for color clustering
Pattern Recognition Letters
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
A Text Detection, Localization and Segmentation System for OCR in Images
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
Wavelet filter evaluation for image compression
IEEE Transactions on Image Processing
Localizing and segmenting text in images and videos
IEEE Transactions on Circuits and Systems for Video Technology
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Textual information present in images can help to achieve the aim of automatic content based annotation and retrieval of images. In this paper, we address the problem of text segmentation (TS) in images with complex background for recognition purposes. The proposed TS method takes as input the localized text and proceeds as follows: First, the number of initial clusters is determined by analyzing the colors of the image. Second, the image pixels are clustered using the number of clusters defined in the first step. The compactness of the clusters is evaluated in each step and improved iteratively to avoid possible oversegmentation of characters. Finally, an algorithm based on a rating scheme is proposed to determine the cluster where the text pixels are classified. The proposed method is evaluated on the basis of recognition results instead of visual segmentation results. Comparative experimental results using a test set of 2684 characters are reported.