TextFinder: An Automatic System to Detect and Recognize Text In Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale Segmentation of Unstructured Document Pages Using Soft Decision Integration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of Text Marks on Moving Vehicles
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Parallel image segmentation in reconfigurable chip multiprocessors
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
Hi-index | 0.00 |
This paper proposes a method that aims to reduce a real scene to a set of regions that contain text fragments and keep small number of false positives. Text is modeled and characterized as a texture pattern, by employing the QMF wavelet decomposition as a texture feature extractor. Processing includes segmentation and spatial selection of regions and then content-based selection of fragments. Unlike many previous works, text fragments in different scales and resolutions laid against complex backgrounds are segmented without supervision. Tested in four image databases, the method is able to reduce visual noise to 4.69% and reaches 96.5% of coherency between the localized fragments and those generated by manual segmentation.