A multilingual, multimodal digital video library system
Proceedings of the 2nd ACM/IEEE-CS joint conference on Digital libraries
A Language for Audiovisual Template Specification and Recognition
CP '02 Proceedings of the 6th International Conference on Principles and Practice of Constraint Programming
Data GroundTruth, Complexity, and Evaluation Measures for Color Document Analysis
DAS '02 Proceedings of the 5th International Workshop on Document Analysis Systems V
Color Text Extraction from Camera-based Images the Impact of the Choice of the Clustering Distance
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Color text extraction with selective metric-based clustering
Computer Vision and Image Understanding
Text detection, localization, and tracking in compressed video
Image Communication
A multifunctional reading assistant for the visually impaired
Journal on Image and Video Processing
A multifunctional reading assistant for the visually impaired
Journal on Image and Video Processing
An Automatic Method for Video Character Segmentation
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
A new video text extraction approach
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A comprehensive method for Arabic video text detection, localization, extraction and recognition
PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
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Text is a very powerful index in content-based image and video indexing. We propose a new text detection and segmentation algorithm that is especially designed for being applied to color images with complicated background. Our goal is to minimize the number of false alarms and to binarize efficiently the detected text areas so that they can be processed by standard OCR software. First, potential areas of text are detected by enhancement and clustering processes, considering most of constraints related to the texture of words. Then, classification and binarization of potential text areas are achieved in a single scheme performing color quantization and characters periodicity analysis. We report a high rate of good detection results with very few false alarms and reliable text binarization.