基于深度学习法的视频文本区域定位与识别

Area Location and Recognition of Video Text Based on Depth Learning Method

通过对视频图像进行快速、准确的文本定位与识别,有利于提高视频信息处理的效率与准确率.采用Gabor滤波器实现在横、竖、撇、捺四个方向上的视频图像的纹理特征的提取,再通过RBM逐层增量深度学习算法构建深度置信网络,实现对提取的纹理特征图像中文本区域的定位.论文同时研究了利用形态学处理方法和OCR字符库实现对视频图像文本识别的可行性,并分析了识别效果.测试结果表明,本文提出的深度学习算法与形态学字符识别方法相结合,不但能够实现对视频图像文本区域的准确定位,还有利于提高字符识别的效率和准确率.

It is advantageous to improve the efficiency and accuracy of video information processing through fast and accurate text area location and recognition of video images.The Gabor filter has been used to extract the texture features of video images in the four directions of horizontal,vertical,left-failing and right-falling.Then,by RBM layer increment depth learning algorithm,a depth belief network has been structured,and at the same time,the text region location for the texture feature images has been realized.The paper also studied the feasibility and recognition effect about using morphological process and OCR character database to realize the video image text recognition.The test results showed that the proposed optimized depth learning algorithm combining with morphology character recognition method can not only realize the accurate location of the text region for video images,but also improve the efficiency and accuracy of the character recognition.