视频字幕验证是字幕检测中的重要环节，其目的在于提高检测准确率。当前的验证方法多是依据经验规则。这些方法在图像背景复杂、图像分辨率低以及字幕字体、大小、颜色多变这些条件下，适应性差。为提高验证方法的适应性和准确性，通过将2维主成分分析（2DPCA）应用到视频字幕验证中，提出了一种基于2DPCA和支撑向量机（SVM）的视频字幕验证方法。该方法分训练和判别两个步骤，即首先采用2DPCA方法提取视频图像块特征，然后通过训练SVM对图像块进行验证和分类。实验结果表明．在图像背景复杂、图像分辨率低以及字幕字体、大小、颜色多变这些传统验证方法或多或少都存在困难的条件下，该方法不仅具有良好的视频字幕验证能力，而且也能明显降低算法的运行耗时。

For improving accuracy,validating text is a key step of detecting text in video.The current approaches mostly based on experiential rules.The approaches are not adaptive,in condition of complex background,low resolution,varied font,size,color of text in video.For improving adaptability and accuracy of validating text,the application of two-dimension principal component analysis(2DPCA) for video frame processing is investigated and a novel 2DPCA and support vector machine(SVM) based approach for validating text in video is proposed.The approach has two steps of training and validating.Firstly,2DPCA is adopted to get the features of video image patches.Then,SVM is trained to validate and classify video image patches.The experimental results illustrate that the novel approach for validating text in video is more effective and costs less time than the other approaches,in condition of complex background,low resolution,varied font,size,color of text in video.