基于特征金字塔网络的自然场景图像文本检测

针对深度学习中对任意形状文本检测准确率不高的问题,提出了一种结合特征金字塔网络(feature pyramid network,FPN)和内核尺度扩展算法的文本检测网络模型.特征金字塔网络能够提取卷积层中更加鲁棒的特征,融合后生成不同尺度的特征内核;内核尺度扩展算法将生成的最小特征内核逐渐扩展为包围完整文本实例的特征图...

Text detection of natural scene images based on feature pyramid network

Aiming at the problem of low accuracy in detecting arbitrary shape text in deep learning, a text detection method combining feature pyramid network (FPN) and kernel scale extension algorithm is proposed in this paper. The feature pyramid network can extract more robust features in the convolutional layer and generate different scale feature kernels after fusion. The kernel scale expansion algorithm gradually expands the generated minimum feature kernel into a feature map which surrounds the entire text instance. At the same time, for the text examples which are difficult to detect in natural scenes, the online hard example mining (OHEM) method was added during the training phase, and two training strategies were used to train in the form of transfer learning. Simulation results show that the algorithm model has good detection performance on different datasets.