图像中的文字自动定位是计算机视觉领域中的一个新兴研究热点。为了使得定位算法能够适应不同类型的图像和文字，根据文字所具有的特殊纹理属性，提出了一种具有普适能力的基于直方图特征和AdaBoost的文字定位算法。该算法首先通过提取对文字具有较强鉴别能力的直方图特征和引入AdaBoost算法来设计级联结构的纹理分类器；然后用该分类器的概率输出来生成文字概率图；在此基础上再通过CAMSHIFT算法得到最终的定位结果。实验结果表明，该算法具有较强的鲁棒性，能够适应文字在语种、字体、尺度等方面的变化，在不同类型的图像中都能得到较好的定位结果。

Automatic text location in images plays an important role in image content understanding, and draws attentions of researchers in the domain of computer vision. Current text location algorithms are mostly adaptive to specific applications; they are sensitive to the variation of text or images and lack robustness. This paper presents a universal approach for text location based on histogram features and AdaBoost. The new algorithm extracts histogram features, which have strong discriminabilities for text and non-text. AdaBoost algorithm with cascade structure is introduced to design the classifier for text texture. The algorithm transfers the binary output of the texture classifier into probability form and generates corresponding text probability image. CAMSHIFT algorithm is used to search for the final location result in the text probability image. The experimental results demonstrate the robustness of the proposed algorithm, which is adaptive to the text of different languages, fonts or scales, and gets promising location results in variant types of images.