Picture Segmentation by a Tree Traversal Algorithm
Journal of the ACM (JACM)
An Introduction to Digital Image Processing
An Introduction to Digital Image Processing
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
Video OCR for Digital News Archive
CAIVD '98 Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases (CAIVD '98)
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text detection and segmentation in complex color images
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 04
Robust Binarization for Video Text Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 02
Localizing and segmenting text in images and videos
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents an automatic segmentation system for characters in text color images cropped from natural images or videos based on a new neuronal architecture insuring fast processing and robustness against noise, variations in illumination, complex background and low resolution. An off-line training phase on a set of synthetic text color images, where the exact character positions are known, allows adjusting the neural parameters and thus building an optimal non linear filter which extracts the best features in order to robustly detect the border positions between characters. The proposed method is tested on a set of synthetic text images to precisely evaluate its performance according to noise, and on a set of complex text images collected from video frames and web pages to evaluate its performance on real images. The results are encouraging with a good segmentation rate of 89.12% and a recognition rate of 81.94% on a set of difficult text images collected from video frames and from web pages.