卷积深度置信网络的场景文本检测

自然场景中的文本检测对于视频、图像和图片等海量信息的检索管理具有重要意义.针对自然场景中的文本检测面临着图像背景复杂、分辨率低和分布随意的问题，提出一种场景文本检测的方法.该方法将最大稳定极值区域算法与卷积深度置信网络进行结合，把从最大稳定极值区域中提取出来的候选文本区域输入到卷积深度置信网络中进行特征提取，由Softmax分类器对提取的特征进行分类.该方法在ICDAR数据集和SVT数据集上进行实验，实验结果表明该方法有助于提高场景文本检测的精确率及召回率.

Scene Text Detection in Convolutional Deep Belief Networks

Text detection in the natural scenes is of great significance to the retrieval and management of large amounts of information such as video, images, and pictures. Depending on the complex background, low resolution and random distribution of the text detection in natural scenes, a scene text detection method was proposed, which combined the maximum stable extremal region algorithm and convolutional deep belief networks. In this method, candidate text region extracted from the maximally stable extremal region entered into the convolutional deep belief network for feature extraction. Then these features were classified by Softmax classifier. Experiments were carried out on ICDAR datasets and SVT datasets, and the experiment results show that the proposed method is helpful for improving the precision and recall rate of scene text detection.