Buffer-text: Detecting arbitrary shaped text in natural scene image

Abstract

Scene text detection has always been a research hotspot in computer vision and image understanding. With the development of deep learning, segmentation-based methods have achieved an exceptional effect in regular or curved text detection, but they cannot separate adjacent word-level texts. In this paper, we proposed a detector called Buffer-Text for the detection of irregular text in the natural scene image. First, the buffer region is proposed for bending text detection which widens the spatial distance between word-level texts. Then, a centerline-based polygon expansion algorithm is developed for the acquisition of the buffer region. After that, the scene text image is divided into different regions which are predicted by adopting the idea of multiclass semantic segmentation. To obtain effective segmentation results and solve the category imbalance problem, a Fully Convolutional Networks (FCN) with Spatial and Channel Squeeze & Excitation Block module is designed, and a loss function with adaptive weight updating is defined for the network. Ultimately, the post-processing including the total erosion and the single expansion is applied to eliminate the areas of noise in the segmented image and to separate the weak junctions in the word-level text. To verify the validity of the proposed method, several experiments were conducted on two curved text datasets, namely Total-Text and CTW1500, and the results indicated that the proposed method achieved significant accuracy in three statistical indicators (precision, recall, and F-score), particularly for the images with natural scenes and various text shapes.