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A new text location method in natural scene images based on color reduction and AdaBoost

机译:一种基于色彩还原和AdaBoost的自然场景图像文本定位新方法

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A new text location method based on color reduction and Adaboost classifier is proposed in this paper, which is used in extracting the text region in natural scene images with complex background. Firstly, the images are segmented into several layers through adopting color reduction and mean shift image segmentation techniques. Then, in order to pick up the potential text region, each layer is processed using connected component analysis, text region identification and text region merging, etc. Finally, HOG (Histogram of Oriented Gradient) and LBP (Local Binary Pattern) features are extracted from the candidate text region, and an AdaBoost classifier is applied to classify text and non-text regions. A series of experiments on a natural scene images database have indicated that, our method can effectively improve the text location in natural scene images with complex background, showing the effectiveness of the proposed approach.
机译:提出了一种基于色彩还原和Adaboost分类器的文本定位新方法,该方法用于提取背景复杂的自然场景图像中的文本区域。首先,通过采用色彩还原和均值偏移图像分割技术将图像分割为几层。然后,为了拾取潜在的文本区域,使用连接的组件分析,文本区域标识和文本区域合并等处理每个图层。最后,提取HOG(定向梯度直方图)和LBP(局部二进制模式)特征从候选文本区域开始,然后应用AdaBoost分类器对文本区域和非文本区域进行分类。在自然场景图像数据库上进行的一系列实验表明,该方法可以有效地改善背景复杂的自然场景图像中的文本位置,证明了该方法的有效性。

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