| Information & Computer Science |
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| A learning-based method to detect and segment text from scene images |
| JIANG Ren-jie, QI Fei-hu, XU Li, WU Guo-rong, ZHU Kai-hua |
| Department of Computer Science and Technology, Shanghai Jiao Tong University, Shanghai 200240, China |
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Abstract 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.
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Received: 01 June 2006
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