Text embedded in images is one of many important cues for indexing and retrieval of images and videos. In the paper, we present a novel method of detecting text aligned either horizontally or vertically, in which a py...Text embedded in images is one of many important cues for indexing and retrieval of images and videos. In the paper, we present a novel method of detecting text aligned either horizontally or vertically, in which a pyramid structure is used to represent an image and the features of the text are extracted using SUSAN edge detector. Text regions at each level of the pyramid are identified according to the autocorrelation analysis. New techniques are introduced to split the text regions into basic ones and merge them into text lines. By evaluating the method on a set of images, we obtain a very good performance of text detection.展开更多
This paper proposes a learning-based method for text detection and text segmentation in natural scene images. First, the input image is decomposed into multiple connected-components (CCs) by Niblack clustering algorit...This paper proposes a learning-based method for text detection and text segmentation in natural scene images. First, the input image is decomposed into multiple connected-components (CCs) by Niblack clustering algorithm. Then all the CCs including text CCs and non-text CCs are verified on their text features by a 2-stage classification module, where most non-text CCs are discarded by an attentional cascade classifier and remaining CCs are further verified by an SVM. All the accepted CCs are output to result in text only binary image. Experiments with many images in different scenes showed satisfactory performance of our proposed method.展开更多
文摘Text embedded in images is one of many important cues for indexing and retrieval of images and videos. In the paper, we present a novel method of detecting text aligned either horizontally or vertically, in which a pyramid structure is used to represent an image and the features of the text are extracted using SUSAN edge detector. Text regions at each level of the pyramid are identified according to the autocorrelation analysis. New techniques are introduced to split the text regions into basic ones and merge them into text lines. By evaluating the method on a set of images, we obtain a very good performance of text detection.
基金Project supported by the OMRON and SJTU Collaborative Founda-tion under PVS project (2005.03~2005.10)
文摘This paper proposes a learning-based method for text detection and text segmentation in natural scene images. First, the input image is decomposed into multiple connected-components (CCs) by Niblack clustering algorithm. Then all the CCs including text CCs and non-text CCs are verified on their text features by a 2-stage classification module, where most non-text CCs are discarded by an attentional cascade classifier and remaining CCs are further verified by an SVM. All the accepted CCs are output to result in text only binary image. Experiments with many images in different scenes showed satisfactory performance of our proposed method.
