The nature of statistical learning theory
The nature of statistical learning theory
TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic location of text in video frames
MULTIMEDIA '01 Proceedings of the 2001 ACM workshops on Multimedia: multimedia information retrieval
Applications of Video-Content Analysis and Retrieval
IEEE MultiMedia
Video OCR: indexing digital new libraries by recognition of superimposed captions
Multimedia Systems - Special section on video libraries
Character Segmentation of Color Images from Digital Camera
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Automatic Text Recognition for Video Indexing
Automatic Text Recognition for Video Indexing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Printed Text and Handwriting Identification in Noisy Document Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Extraction and recognition of artificial text in multimedia documents
Pattern Analysis & Applications
Fast and robust text detection in images and video frames
Image and Vision Computing
Detecting and reading text in natural scenes
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automatic text detection and tracking in digital video
IEEE Transactions on Image Processing
A comprehensive method for multilingual video text detection, localization, and extraction
IEEE Transactions on Circuits and Systems for Video Technology
Text detection in images using sparse representation with discriminative dictionaries
Image and Vision Computing
Localizing slab identification numbers in factory scene images
Expert Systems with Applications: An International Journal
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In this paper, we propose a new approach for accurate text localization in images based on SVM (support vector machine) output scores. In general, SVM output scores for the verification of text candidates provide a measure of the closeness to the text. Up to the present, most researchers used the score for verifying the text candidate region whether it is text or not. However, we use the output score for refining the initial localized text lines and selecting the best localization result from the different pyramid levels. By means of the proposed approach, we can obtain more accurate text localization results. Our method has three modules: (1) text candidate detection based on edge-CCA (connected component analysis), (2) text candidate verification based on the classifier fusion of N-gray (normalized gray intensity) and CGV (constant gradient variance), and (3) text line refinement based on the SVM output score, color distribution and prior geometric knowledge. By means of experiments on a large news database, we demonstrate that our method achieves impressive performance with respect to the accuracy, robustness and efficiency.